Graph Classification | Papers With Code

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Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph. <span style="color:grey; opacity: 0.6">( Image credit: [Hierarchical Graph Pooling with Structure Learning](https://github.com/cszhangzhen/HGP-SL) )</span>" />  <meta property="og:title" content="Papers with Code - Graph Classification"> <meta property="og:description" content="**Graph Classification** is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph. <span style="color:grey; opacity: 0.6">( Image credit: [Hierarchical Graph Pooling with Structure Learning](https://github.com/cszhangzhen/HGP-SL) )</span>"> <meta property="og:image" content="https://production-media.paperswithcode.com/tasks/Screenshot_2019-11-22_at_20.47.26_NEuBvdP.png"> <meta property="og:url" content="https://paperswithcode.com/task/graph-classification">  <meta name="twitter:card" content="summary_large_image"> <meta name="twitter:site" content="@paperswithcode"> <meta name="twitter:title" content="Papers with Code - Graph Classification"> <meta name="twitter:description" content="**Graph Classification** is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph. <span style="color:grey; opacity: 0.6">( Image credit: [Hierarchical Graph Pooling with Structure Learning](https://github.com/cszhangzhen/HGP-SL) )</span>"> <meta name="twitter:creator" content="@paperswithcode"> <meta name="twitter:url" content="https://paperswithcode.com/task/graph-classification"> <meta name="twitter:domain" content="paperswithcode.com">  <script type="application/ld+json">{ "@context": "http://schema.org", "@graph": { "@type": "CreativeWork", "@id": "graph-classification", "name": "Graph Classification", "description": "**Graph Classification** is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.\r\n\r\n\u003Cspan style=\"color:grey; opacity: 0.6\"\u003E( Image credit: [Hierarchical Graph Pooling with Structure Learning](https://github.com/cszhangzhen/HGP-SL) )\u003C/span\u003E", "url": "https://paperswithcode.com/task/graph-classification", "image": "https://production-media.paperswithcode.com/tasks/Screenshot_2019-11-22_at_20.47.26_NEuBvdP.png", "subjectOf": [ { "@type": "CreativeWork", "@id": "graphs", "name": "Graphs", "description": "Browse 112 tasks \u2022 311 datasets \u2022 538 ", "image": "https://paperswithcode.com/static/sota.jpeg", "headline": "Browse state-of-the-art in ML leaderboards \u2022 11336 papers with code." }, { "@type": "CreativeWork", "@id": "classification-1", "name": "Classification", "description": "**Classification** is the task of categorizing a set of data into predefined classes or groups. The aim of classification is to train a model to correctly predict the class or group of new, unseen data. The model is trained on a labeled dataset where each instance is assigned a class label. The learning algorithm then builds a mapping between the features of the data and the class labels. This mapping is then used to predict the class label of new, unseen data points. The quality of the prediction is usually evaluated using metrics such as accuracy, precision, and recall.", "image": "https://production-media.paperswithcode.com/tasks/31f6d31f-a82e-4043-9e13-c9eb57274f19.png", "subjectOf": [ { "@type": "CreativeWork", "@id": "methodology", "name": "Methodology", "description": "Browse 207 tasks \u2022 1245 datasets \u2022 908 ", "image": "https://paperswithcode.com/static/sota.jpeg", "headline": "Browse state-of-the-art in ML leaderboards \u2022 58398 papers with code." } ], "headline": "Classification" } ], "headline": "Graph Classification" } }</script> <meta name="theme-color" content="#fff"/> <link rel="manifest" href="https://production-assets.paperswithcode.com/static/manifest.web.json"> </head> <body> <nav class="navbar navbar-expand-lg navbar-light header"> <a class="navbar-brand" href="/"> <span class=" icon-wrapper" data-name="pwc"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path d="M88 128h48v256H88zm144 0h48v256h-48zm-72 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<option value="">---------</option> <option value="3323" selected>Classification</option> </select> </div> </div> <div id="div_id_description" class="form-group"> <label for="id_description" class=""> Description with markdown (optional): </label> <div class=""> <textarea name="description" cols="40" rows="3" class="textarea form-control" id="id_description"> **Graph Classification** is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph. <span style="color:grey; opacity: 0.6">( Image credit: [Hierarchical Graph Pooling with Structure Learning](https://github.com/cszhangzhen/HGP-SL) )</span></textarea> </div> </div> <div id="div_id_image" class="form-group"> <label for="id_image" class=""> Image </label> <div class=""> Currently: <a href="https://production-media.paperswithcode.com/tasks/Screenshot_2019-11-22_at_20.47.26_NEuBvdP.png">tasks/Screenshot_2019-11-22_at_20.47.26_NEuBvdP.png</a> <input type="checkbox" name="image-clear" id="image-clear_id"> <label for="image-clear_id">Clear</label><br> Change: <input type="file" name="image" accept="image/*" class="clearablefileinput form-control-file" id="id_image"> </div> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary"> Submit </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" 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data-name="images"><svg viewBox="0 0 576 514.999" xmlns="http://www.w3.org/2000/svg"><path d="M480 417.998v16c0 26.51-21.49 48-48 48H48c-26.51 0-48-21.49-48-48v-256c0-26.51 21.49-48 48-48h16v208c0 44.113 35.888 80 80 80h336zm96-80c0 26.51-21.49 48-48 48H144c-26.51 0-48-21.49-48-48v-256c0-26.51 21.49-48 48-48h384c26.51 0 48 21.49 48 48v256zm-320-208c0-26.51-21.49-48-48-48s-48 21.49-48 48 21.49 48 48 48 48-21.49 48-48zm-96 144v48h352v-112l-87.514-87.514c-4.687-4.687-12.285-4.687-16.971 0L272 257.999l-39.514-39.515c-4.688-4.686-12.285-4.686-16.972 0z"/></svg></span> <span>Graphs</span> </span> </a> </div> <div class="artefact-header"> <div class="float-right task-edit"> <div class="dropdown edit-button"> <a data-bs-toggle="modal" data-bs-target="#loginModal"> <span class="badge badge-method-edit" style="padding-top:10px;"><span class=" icon-wrapper icon-fa icon-fa-solid" data-name="edit"><svg viewBox="0 0 576 514.999" xmlns="http://www.w3.org/2000/svg"><path d="M402.6 85.198l90.2 90.2c3.8 3.8 3.8 10 0 13.8l-218.399 218.4-92.8 10.3c-12.4 1.4-22.9-9.1-21.5-21.5l10.3-92.8 218.4-218.4c3.799-3.8 10-3.8 13.799 0zm162-22.9c15.2 15.2 15.2 39.9 0 55.2l-35.4 35.4c-3.8 3.8-10 3.8-13.8 0l-90.2-90.2c-3.8-3.8-3.8-10 0-13.8l35.4-35.4c15.3-15.2 40-15.2 55.2 0zM384 348.198c0-3.2 1.3-6.2 3.5-8.5l40-40c7.6-7.5 20.5-2.2 20.5 8.5v157.8c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48v-352c0-26.5 21.5-48 48-48h285.8c10.7 0 16.1 12.9 8.5 20.5l-40 40c-2.3 2.2-5.3 3.5-8.5 3.5H64v320h320v-101.8z"/></svg></span> Edit</span> </a> </div> </div> <h1 id="task-home">Graph Classification</h1> <div class="artefact-information"> <p> 459 papers with code • 72 benchmarks • 52 datasets </p> </div> </div> <div class="col-lg-9">  <div class="description"> <div class="description-content"> <p><strong>Graph Classification</strong> is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.</p> <p><span style="color: grey;">( Image credit: <a href="https://github.com/cszhangzhen/HGP-SL">Hierarchical Graph Pooling with Structure Learning</a> )</span></p> </div> </div>  <div class="image-container task-image-mobile"> <a href="https://production-media.paperswithcode.com/thumbnails/task/2ea42743-71c0-4f81-995d-ce9dc4bd63b0.jpg" data-lightbox="imageresource"> <img id="imageresource" width=100% src="https://production-media.paperswithcode.com/thumbnails/task/2ea42743-71c0-4f81-995d-ce9dc4bd63b0.jpg"> </a> </div>  <div class="task-benchmarks"> <div id="benchmarks" class="collapsed"> <div class="title"> <h2 id="benchmarks">Benchmarks <div class="float-right"> <div class="dropdown edit-button task-add-a-result"> <a data-bs-toggle="modal" data-bs-target="#loginModal"> <span class="badge badge-primary" style="font-size:12px;"> Add a Result</span> </a> </div> </div> </h2> These leaderboards are used to track progress in Graph Classification <hr> </div> <div class="sota-table-preview table-responsive"> <table id="benchmarksTable" class="table-striped table-responsive"> <thead> <tr> <th>Trend</th> <th style="padding-left:12px;">Dataset</th> <th style="min-width:200px">Best Model</th>  <th class="text-center">Paper</th> <th class="text-center">Code</th> <th class="text-center">Compare</th> </tr> </thead> <tbody> <tr onclick="window.location='/sota/graph-classification-on-proteins';"> <td> <a href="/sota/graph-classification-on-proteins"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-proteins-small_0dfe7e49.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-proteins"> PROTEINS </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-proteins"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> HGP-SL </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/hierarchical-graph-pooling-with-structure"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/hierarchical-graph-pooling-with-structure#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-proteins" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-mutag';"> <td> <a href="/sota/graph-classification-on-mutag"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-mutag-small_64284141.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-mutag"> MUTAG </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-mutag"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> Evolution of Graph Classifiers </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/evolution-of-graph-classifiers"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/evolution-of-graph-classifiers#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-mutag" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-nci1';"> <td> <a href="/sota/graph-classification-on-nci1"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-nci1-small_6092e5f1.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-nci1"> NCI1 </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-nci1"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> TFGW ADJ (L=2) </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/template-based-graph-neural-network-with"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/template-based-graph-neural-network-with#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-nci1" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-enzymes';"> <td> <a href="/sota/graph-classification-on-enzymes"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-enzymes-small_2bf48e87.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-enzymes"> ENZYMES </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-enzymes"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> ESA (Edge set attention, no positional encodings) </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/masked-attention-is-all-you-need-for-graphs"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-enzymes" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-dd';"> <td> <a href="/sota/graph-classification-on-dd"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-dd-small_1803fb2e.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-dd"> D&D </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-dd"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> U2GNN (Unsupervised) </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/unsupervised-universal-self-attention-network"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/unsupervised-universal-self-attention-network#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-dd" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-imdb-b';"> <td> <a href="/sota/graph-classification-on-imdb-b"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-imdb-b-small_fab1a412.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-imdb-b"> IMDb-B </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-imdb-b"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> U2GNN (Unsupervised) </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/unsupervised-universal-self-attention-network"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/unsupervised-universal-self-attention-network#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-imdb-b" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-peptides-func';"> <td> <a href="/sota/graph-classification-on-peptides-func"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-peptides-func-small_0c46eca3.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-peptides-func"> Peptides-func </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-peptides-func"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> ESA + RWSE (Edge set attention, Random Walk Structural Encoding, + validation set) </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/masked-attention-is-all-you-need-for-graphs"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-peptides-func" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-collab';"> <td> <a href="/sota/graph-classification-on-collab"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-collab-small_14409b53.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-collab"> COLLAB </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-collab"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> U2GNN (Unsupervised) </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/unsupervised-universal-self-attention-network"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/unsupervised-universal-self-attention-network#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-collab" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-nci109';"> <td> <a href="/sota/graph-classification-on-nci109"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-nci109-small_1ed1122b.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-nci109"> NCI109 </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-nci109"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> WKPI-kcenters </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/learning-metrics-for-persistence-based-2"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/learning-metrics-for-persistence-based-2#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-nci109" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-ptc';"> <td> <a href="/sota/graph-classification-on-ptc"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-ptc-small_876cf210.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-ptc"> PTC </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-ptc"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> U2GNN (Unsupervised) </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/unsupervised-universal-self-attention-network"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/unsupervised-universal-self-attention-network#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-ptc" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-imdb-m';"> <td> <a href="/sota/graph-classification-on-imdb-m"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-imdb-m-small_d175b1cc.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-imdb-m"> IMDb-M </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-imdb-m"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> U2GNN (Unsupervised) </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/unsupervised-universal-self-attention-network"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/unsupervised-universal-self-attention-network#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-imdb-m" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-cifar10-100k';"> <td> <a href="/sota/graph-classification-on-cifar10-100k"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-cifar10-100k-small_9a52a00a.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-cifar10-100k"> CIFAR10 100k </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-cifar10-100k"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> NeuralWalker </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/learning-long-range-dependencies-on-graphs"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/learning-long-range-dependencies-on-graphs#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-cifar10-100k" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-mnist';"> <td> <a href="/sota/graph-classification-on-mnist"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-mnist-small_17d995b3.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-mnist"> MNIST </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-mnist"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> ESA (Edge set attention, no positional encodings, tuned) </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/masked-attention-is-all-you-need-for-graphs"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-mnist" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-reddit-b';"> <td> <a href="/sota/graph-classification-on-reddit-b"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-reddit-b-small_8ea779cb.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-reddit-b"> REDDIT-B </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-reddit-b"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> CRaWl </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/graph-learning-with-1d-convolutions-on-random"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/graph-learning-with-1d-convolutions-on-random#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-reddit-b" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-re-m5k';"> <td> <a href="/sota/graph-classification-on-re-m5k"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-re-m5k-small_eaf77254.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-re-m5k"> RE-M5K </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-re-m5k"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GIN-0 </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/how-powerful-are-graph-neural-networks"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/how-powerful-are-graph-neural-networks#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-re-m5k" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-upfd-pol';"> <td> <a href="/sota/graph-classification-on-upfd-pol"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-upfd-pol-small_82f10574.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-upfd-pol"> UPFD-POL </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-upfd-pol"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> HGFND </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/nothing-stands-alone-relational-fake-news-2"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/nothing-stands-alone-relational-fake-news-2#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-upfd-pol" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-upfd-gos';"> <td> <a href="/sota/graph-classification-on-upfd-gos"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-upfd-gos-small_403d1715.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-upfd-gos"> UPFD-GOS </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-upfd-gos"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> UPFD-SAGE </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/user-preference-aware-fake-news-detection"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/user-preference-aware-fake-news-detection#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-upfd-gos" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-reddit-binary';"> <td> <a href="/sota/graph-classification-on-reddit-binary"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-reddit-binary-small_30aba0af.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-reddit-binary"> REDDIT-BINARY </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-reddit-binary"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> R-GIN + PANDA </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/panda-expanded-width-aware-message-passing"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/panda-expanded-width-aware-message-passing#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-reddit-binary" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-imdb-binary';"> <td> <a href="/sota/graph-classification-on-imdb-binary"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-imdb-binary-small_ae860993.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-imdb-binary"> IMDB-BINARY </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-imdb-binary"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GIN + PANDA </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/panda-expanded-width-aware-message-passing"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/panda-expanded-width-aware-message-passing#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-imdb-binary" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-re-m12k';"> <td> <a href="/sota/graph-classification-on-re-m12k"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-re-m12k-small_31041999.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-re-m12k"> RE-M12K </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-re-m12k"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GFN-light </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/dissecting-graph-neural-networks-on-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/dissecting-graph-neural-networks-on-graph#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-re-m12k" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-hiv-dataset';"> <td> <a href="/sota/graph-classification-on-hiv-dataset"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-hiv-dataset-small_547ae005.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-hiv-dataset"> HIV dataset </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-hiv-dataset"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> CIN++ </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/cin-enhancing-topological-message-passing"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/cin-enhancing-topological-message-passing#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-hiv-dataset" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-neuron-binary';"> <td> <a href="/sota/graph-classification-on-neuron-binary"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-neuron-binary-small_7d1270fd.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-neuron-binary"> NEURON-BINARY </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-neuron-binary"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> WKPI-kmeans </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/learning-metrics-for-persistence-based-2"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/learning-metrics-for-persistence-based-2#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-neuron-binary" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-neuron-multi';"> <td> <a href="/sota/graph-classification-on-neuron-multi"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-neuron-multi-small_8c62bc6b.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-neuron-multi"> NEURON-MULTI </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-neuron-multi"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> WKPI-kcenters </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/learning-metrics-for-persistence-based-2"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/learning-metrics-for-persistence-based-2#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-neuron-multi" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-neuron-average';"> <td> <a href="/sota/graph-classification-on-neuron-average"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-neuron-average-small_045a7219.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-neuron-average"> NEURON-Average </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-neuron-average"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> WKPI-kcenters </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/learning-metrics-for-persistence-based-2"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/learning-metrics-for-persistence-based-2#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-neuron-average" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-frankenstein';"> <td> <a href="/sota/graph-classification-on-frankenstein"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-frankenstein-small_55d33bf0.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-frankenstein"> FRANKENSTEIN </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-frankenstein"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GWL_WL </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/graph-invariant-kernels"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-frankenstein" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-hiv-fmri-77';"> <td> <a href="/sota/graph-classification-on-hiv-fmri-77"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-hiv-fmri-77-small_0da7b750.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-hiv-fmri-77"> HIV-fMRI-77 </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-hiv-fmri-77"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> IsoNN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/isonn-isomorphic-neural-network-for-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/isonn-isomorphic-neural-network-for-graph#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-hiv-fmri-77" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-hiv-dti-77';"> <td> <a href="/sota/graph-classification-on-hiv-dti-77"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-hiv-dti-77-small_1c36340d.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-hiv-dti-77"> HIV-DTI-77 </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-hiv-dti-77"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> IsoNN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/isonn-isomorphic-neural-network-for-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/isonn-isomorphic-neural-network-for-graph#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-hiv-dti-77" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-bp-fmri-97';"> <td> <a href="/sota/graph-classification-on-bp-fmri-97"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-bp-fmri-97-small_ac8091e5.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-bp-fmri-97"> BP-fMRI-97 </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-bp-fmri-97"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> IsoNN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/isonn-isomorphic-neural-network-for-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/isonn-isomorphic-neural-network-for-graph#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-bp-fmri-97" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-mutagenicity';"> <td> <a href="/sota/graph-classification-on-mutagenicity"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-mutagenicity-small_3bf0bd4d.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-mutagenicity"> Mutagenicity </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-mutagenicity"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> TREE-G </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/graph-trees-with-attention"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/graph-trees-with-attention#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-mutagenicity" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-malnet-tiny';"> <td> <a href="/sota/graph-classification-on-malnet-tiny"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-malnet-tiny-small_4c86babf.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-malnet-tiny"> MalNet-Tiny </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-malnet-tiny"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GatedGCN+ </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/unlocking-the-potential-of-classic-gnns-for"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/unlocking-the-potential-of-classic-gnns-for#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-malnet-tiny" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-reddit-multi-12k';"> <td> <a href="/sota/graph-classification-on-reddit-multi-12k"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-reddit-multi-12k-small_30fd6514.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-reddit-multi-12k"> REDDIT-MULTI-12K </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-reddit-multi-12k"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GNN (DiffPool) </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/hierarchical-graph-representation-learning"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/hierarchical-graph-representation-learning#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-reddit-multi-12k" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-hiv';"> <td> <a href="/sota/graph-classification-on-hiv"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-hiv-small_4c8c0a79.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-hiv"> HIV </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-hiv"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GTOT-Tuning </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/fine-tuning-graph-neural-networks-via-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/fine-tuning-graph-neural-networks-via-graph#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-hiv" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-tox21';"> <td> <a href="/sota/graph-classification-on-tox21"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-tox21-small_ba523531.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-tox21"> Tox21 </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-tox21"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GMT </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/accurate-learning-of-graph-representations-1"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/accurate-learning-of-graph-representations-1#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-tox21" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-toxcast';"> <td> <a href="/sota/graph-classification-on-toxcast"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-toxcast-small_d7de2f48.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-toxcast"> ToxCast </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-toxcast"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GMT </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/accurate-learning-of-graph-representations-1"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/accurate-learning-of-graph-representations-1#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-toxcast" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-bbbp';"> <td> <a href="/sota/graph-classification-on-bbbp"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-bbbp-small_55dd96ec.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-bbbp"> BBBP </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-bbbp"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> G-Tuning </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/fine-tuning-graph-neural-networks-by"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/fine-tuning-graph-neural-networks-by#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-bbbp" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-cox2';"> <td> <a href="/sota/graph-classification-on-cox2"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-cox2-small_09c5b19a.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-cox2"> COX2 </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-cox2"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GIN-0 </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/how-powerful-are-graph-neural-networks"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/how-powerful-are-graph-neural-networks#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-cox2" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-synthetic-dynamic';"> <td> <a href="/sota/graph-classification-on-synthetic-dynamic"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-synthetic-dynamic-small_19ca5b92.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-synthetic-dynamic"> Synthetic Dynamic Networks </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-synthetic-dynamic"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> Time-cohort Dynamic Features + Static Features </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/learning-the-mechanisms-of-network-growth"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/learning-the-mechanisms-of-network-growth#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-synthetic-dynamic" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-ipc-grounded';"> <td> <a href="/sota/graph-classification-on-ipc-grounded"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-ipc-grounded-small_542fc6b1.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-ipc-grounded"> IPC-grounded </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-ipc-grounded"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GCN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/semi-supervised-classification-with-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/semi-supervised-classification-with-graph#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-ipc-grounded" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-ipc-lifted';"> <td> <a href="/sota/graph-classification-on-ipc-lifted"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-ipc-lifted-small_f8ad2d9f.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-ipc-lifted"> IPC-lifted </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-ipc-lifted"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GCN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/semi-supervised-classification-with-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/semi-supervised-classification-with-graph#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-ipc-lifted" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-sider';"> <td> <a href="/sota/graph-classification-on-sider"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-sider-small_6087c4ae.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-sider"> SIDER </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-sider"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GTOT-Tuning </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/fine-tuning-graph-neural-networks-via-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/fine-tuning-graph-neural-networks-via-graph#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-sider" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-clintox';"> <td> <a href="/sota/graph-classification-on-clintox"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-clintox-small_664abb15.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-clintox"> clintox </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-clintox"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> G-Tuning </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/fine-tuning-graph-neural-networks-by"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/fine-tuning-graph-neural-networks-by#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-clintox" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-muv';"> <td> <a href="/sota/graph-classification-on-muv"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-muv-small_dda92d05.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-muv"> MUV </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-muv"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GTOT-Tuning </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/fine-tuning-graph-neural-networks-via-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/fine-tuning-graph-neural-networks-via-graph#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-muv" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-bace';"> <td> <a href="/sota/graph-classification-on-bace"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-bace-small_61b6c7e9.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-bace"> BACE </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-bace"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> G-Tuning </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/fine-tuning-graph-neural-networks-by"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/fine-tuning-graph-neural-networks-by#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-bace" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-20news';"> <td> <a href="/sota/graph-classification-on-20news"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-20news-small_3a647d8f.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-20news"> 20NEWS </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-20news"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> sKNN-LDS </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/neighborhood-enlargement-in-graph-neural"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/neighborhood-enlargement-in-graph-neural#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-20news" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-nci33';"> <td> <a href="/sota/graph-classification-on-nci33"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-nci33-small_0ce06bf5.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-nci33"> NCI33 </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-nci33"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GAM </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/graph-classification-using-structural"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/graph-classification-using-structural#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-nci33" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-nci-83';"> <td> <a href="/sota/graph-classification-on-nci-83"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-nci-83-small_b4c88f22.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-nci-83"> NCI-83 </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-nci-83"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GAM </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/graph-classification-using-structural"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/graph-classification-using-structural#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-nci-83" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-nci-123';"> <td> <a href="/sota/graph-classification-on-nci-123"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-nci-123-small_462878db.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-nci-123"> NCI-123 </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-nci-123"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GAM </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/graph-classification-using-structural"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/graph-classification-using-structural#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-nci-123" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-nc1';"> <td> <a href="/sota/graph-classification-on-nc1"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-nc1-small_d19fb6d3.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-nc1"> NC1 </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-nc1"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> EigenGCN-3 </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/graph-convolutional-networks-with"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/graph-convolutional-networks-with#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-nc1" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-coil-rag';"> <td> <a href="/sota/graph-classification-on-coil-rag"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-coil-rag-small_3815e9bd.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-coil-rag"> COIL-RAG </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-coil-rag"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> SPI-GCN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/spi-gcn-a-simple-permutation-invariant-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-coil-rag" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-synthie';"> <td> <a href="/sota/graph-classification-on-synthie"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-synthie-small_da87e8d0.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-synthie"> SYNTHIE </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-synthie"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> SPI-GCN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/spi-gcn-a-simple-permutation-invariant-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-synthie" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-hydrides';"> <td> <a href="/sota/graph-classification-on-hydrides"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-hydrides-small_b7bc2bdf.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-hydrides"> HYDRIDES </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-hydrides"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> SPI-GCN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/spi-gcn-a-simple-permutation-invariant-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-hydrides" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-reddit-multi-5k';"> <td> <a href="/sota/graph-classification-on-reddit-multi-5k"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-reddit-multi-5k-small_b6c2f918.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-reddit-multi-5k"> REDDIT-MULTI-5k </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-reddit-multi-5k"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GraphSAGE </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/a-fair-comparison-of-graph-neural-networks-1"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/a-fair-comparison-of-graph-neural-networks-1#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-reddit-multi-5k" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-web';"> <td> <a href="/sota/graph-classification-on-web"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-web-small_606ce629.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-web"> Web </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-web"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> UGraphEmb-F </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/unsupervised-inductive-whole-graph-embedding"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/unsupervised-inductive-whole-graph-embedding#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-web" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-aids';"> <td> <a href="/sota/graph-classification-on-aids"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-aids-small_38a8cba5.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-aids"> AIDS </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-aids"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> DGCNN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/dgcnn-disordered-graph-convolutional-neural"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 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</div> </td>  <td> <div class="text-center paper"> <a href="/paper/neighborhood-enlargement-in-graph-neural"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/neighborhood-enlargement-in-graph-neural#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-wine" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-cancer';"> <td> <a href="/sota/graph-classification-on-cancer"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-cancer-small_8088744c.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-cancer"> Cancer </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-cancer"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> sKNN-LDS </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/neighborhood-enlargement-in-graph-neural"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/neighborhood-enlargement-in-graph-neural#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-cancer" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-digits';"> <td> <a href="/sota/graph-classification-on-digits"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-digits-small_79d6df88.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-digits"> Digits </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-digits"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> sKNN-LDS </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/neighborhood-enlargement-in-graph-neural"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 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5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-digits" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-citeseer';"> <td> <a href="/sota/graph-classification-on-citeseer"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-citeseer-small_7d899e27.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-citeseer"> Citeseer </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-citeseer"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> sKNN-LDS </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/neighborhood-enlargement-in-graph-neural"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/neighborhood-enlargement-in-graph-neural#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-citeseer" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-cora';"> <td> <a href="/sota/graph-classification-on-cora"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-cora-small_f7e0c9bd.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-cora"> Cora </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-cora"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> sKNN-LDS </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/neighborhood-enlargement-in-graph-neural"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/neighborhood-enlargement-in-graph-neural#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-cora" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-hiv-fmri-77-1';"> <td> <a href="/sota/graph-classification-on-hiv-fmri-77-1"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-hiv-fmri-77-1-small_7aab0135.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-hiv-fmri-77-1"> HIV-fMRI-77 </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-hiv-fmri-77-1"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> IsoNN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/isonn-isomorphic-neural-network-for-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/isonn-isomorphic-neural-network-for-graph#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-hiv-fmri-77-1" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-5pt-bench-easy';"> <td> <a href="/sota/graph-classification-on-5pt-bench-easy"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-5pt-bench-easy-small_4e74f848.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-5pt-bench-easy"> 5pt. Bench-Easy </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-5pt-bench-easy"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> NDP </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/hierarchical-representation-learning-in-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/hierarchical-representation-learning-in-graph#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 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</tr> <tr onclick="window.location='/sota/graph-classification-on-bench-hard';"> <td> <a href="/sota/graph-classification-on-bench-hard"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-bench-hard-small_bb71af2b.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-bench-hard"> Bench-hard </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-bench-hard"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> NDP </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/hierarchical-representation-learning-in-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/hierarchical-representation-learning-in-graph#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-bench-hard" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-csl';"> <td> <a href="/sota/graph-classification-on-csl"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-csl-small_11d56047.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-csl"> CSL </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-csl"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> CIN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/weisfeiler-and-lehman-go-cellular-cw-networks"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/weisfeiler-and-lehman-go-cellular-cw-networks#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-csl" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-pubmed';"> <td> <a href="/sota/graph-classification-on-pubmed"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-pubmed-small_33796ec8.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-pubmed"> Pubmed </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-pubmed"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> Fea2Fea-s3 </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/fea2fea-exploring-structural-feature"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/fea2fea-exploring-structural-feature#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-pubmed" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-bzr';"> <td> <a href="/sota/graph-classification-on-bzr"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-bzr-small_e619059f.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-bzr"> BZR </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-bzr"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> GDL-g (ADJ) </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/online-graph-dictionary-learning"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/online-graph-dictionary-learning#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-bzr" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-cifar-10';"> <td> <a href="/sota/graph-classification-on-cifar-10"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-cifar-10-small_c5957449.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-cifar-10"> CIFAR-10 </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-cifar-10"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> CKGCN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/ckgconv-general-graph-convolution-with"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/ckgconv-general-graph-convolution-with#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-cifar-10" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-adni';"> <td> <a href="/sota/graph-classification-on-adni"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-adni-small_892c61ed.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-adni"> ADNI </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-adni"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> NeuroPath </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/neuropath-a-neural-pathway-transformer-for"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/neuropath-a-neural-pathway-transformer-for#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-adni" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-oasis';"> <td> <a href="/sota/graph-classification-on-oasis"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-oasis-small_d4bcb7ef.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-oasis"> OASIS </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-oasis"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> NeuroPath </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/neuropath-a-neural-pathway-transformer-for"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/neuropath-a-neural-pathway-transformer-for#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-oasis" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-hcp-aging';"> <td> <a href="/sota/graph-classification-on-hcp-aging"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-hcp-aging-small_8bfbcfe9.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-hcp-aging"> HCP Aging </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-hcp-aging"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> NeuroPath </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/neuropath-a-neural-pathway-transformer-for"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/neuropath-a-neural-pathway-transformer-for#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-hcp-aging" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-uk-biobank-brain-mri';"> <td> <a href="/sota/graph-classification-on-uk-biobank-brain-mri"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-uk-biobank-brain-mri-small_3a0dfa07.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-uk-biobank-brain-mri"> UK Biobank Brain MRI </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-uk-biobank-brain-mri"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> NeuroPath </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/neuropath-a-neural-pathway-transformer-for"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/neuropath-a-neural-pathway-transformer-for#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-uk-biobank-brain-mri" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-msrc-21-per-class';"> <td> <a href="/sota/graph-classification-on-msrc-21-per-class"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-msrc-21-per-class-small_9e8cfaf1.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-msrc-21-per-class"> MSRC-21 (per-class) </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-msrc-21-per-class"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> G-Tuning </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/fine-tuning-graph-neural-networks-by"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/fine-tuning-graph-neural-networks-by#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-msrc-21-per-class" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/graph-classification-on-reddit-12k';"> <td> <a href="/sota/graph-classification-on-reddit-12k"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-classification-on-reddit-12k-small_d4e3a747.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-classification-on-reddit-12k"> REDDIT-12K </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-classification-on-reddit-12k"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> G-Tuning </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/fine-tuning-graph-neural-networks-by"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/fine-tuning-graph-neural-networks-by#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/graph-classification-on-reddit-12k" class="btn btn-primary">See all</a> </div> </td> </tr> </tbody> </table> <div class="table-options expand"> <a href="javascript:void(0)" onclick="document.getElementById('benchmarks').classList.remove('collapsed')" >Show all 72 benchmarks</a> </div> <div class="table-options collapse"> <a href="javascript:void(0)" onclick="document.getElementById('benchmarks').classList.add('collapsed')" >Collapse benchmarks</a> </div> </div> </div> </div>  <div class="task-started"> <div class="title task-libraries"> <h2 id="task-libraries">Libraries <span class="lib-info" data-bs-toggle="popover" data-bs-placement="top" data-bs-trigger="hover" data-bs-title="Libraries" data-bs-content="These libraries are updated daily, based on the papers assigned to this task. If you think a Library is missing, make sure this library is added as code to the papers it implements, and that the papers have been assigned to this task." ><span class=" icon-wrapper icon-ion" data-name="information-circle-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M248 64C146.39 64 64 146.39 64 248s82.39 184 184 184 184-82.39 184-184S349.61 64 248 64z" fill="none" stroke="#000" stroke-miterlimit="10" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M220 220h32v116"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-miterlimit="10" stroke-width="32" d="M208 340h88"/><path d="M248 130a26 26 0 1 0 26 26 26 26 0 0 0-26-26z"/></svg></span></span> </h2> Use these libraries to find Graph Classification models and implementations <hr> <div id="libraries-short-list"> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/benedekrozemberczki/karateclub" onclick="captureOutboundLink('https://github.com/benedekrozemberczki/karateclub'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> benedekrozemberczki/karateclub </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 8 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 2,198 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/rusty1s/pytorch_geometric" onclick="captureOutboundLink('https://github.com/rusty1s/pytorch_geometric'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> rusty1s/pytorch_geometric </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 3 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 21,919 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/lukecavabarrett/pna" onclick="captureOutboundLink('https://github.com/lukecavabarrett/pna'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> lukecavabarrett/pna </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 3 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 346 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/IllinoisGraphBenchmark/IGB-Datasets" onclick="captureOutboundLink('https://github.com/IllinoisGraphBenchmark/IGB-Datasets'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> IllinoisGraphBenchmark/IGB-Datasets </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 3 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 79 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="table-options"> <a href="#" id="libraries-see-more-trigger">See all 18</a> libraries. </div> </div> <div id="libraries-full-list" style="display:none"> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/benedekrozemberczki/karateclub" onclick="captureOutboundLink('https://github.com/benedekrozemberczki/karateclub'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> benedekrozemberczki/karateclub </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 8 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 2,198 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/rusty1s/pytorch_geometric" onclick="captureOutboundLink('https://github.com/rusty1s/pytorch_geometric'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> rusty1s/pytorch_geometric </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 3 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 21,919 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/lukecavabarrett/pna" onclick="captureOutboundLink('https://github.com/lukecavabarrett/pna'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> lukecavabarrett/pna </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 3 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 346 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/IllinoisGraphBenchmark/IGB-Datasets" onclick="captureOutboundLink('https://github.com/IllinoisGraphBenchmark/IGB-Datasets'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> IllinoisGraphBenchmark/IGB-Datasets </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 3 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 79 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/ventr1c/ugba" onclick="captureOutboundLink('https://github.com/ventr1c/ugba'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> ventr1c/ugba </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 3 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 57 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/paulmorio/geo2dr" onclick="captureOutboundLink('https://github.com/paulmorio/geo2dr'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> paulmorio/geo2dr </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 3 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 45 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/safe-graph/GNN-FakeNews" onclick="captureOutboundLink('https://github.com/safe-graph/GNN-FakeNews'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> safe-graph/GNN-FakeNews </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 2 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 474 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/massquantity/LibRecommender" onclick="captureOutboundLink('https://github.com/massquantity/LibRecommender'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> massquantity/LibRecommender </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 2 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 407 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/diningphil/gnn-comparison" onclick="captureOutboundLink('https://github.com/diningphil/gnn-comparison'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> diningphil/gnn-comparison </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 2 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 394 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/twitter-research/cwn" onclick="captureOutboundLink('https://github.com/twitter-research/cwn'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> twitter-research/cwn </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 2 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 158 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/Saro00/DGN" onclick="captureOutboundLink('https://github.com/Saro00/DGN'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> Saro00/DGN </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 2 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 117 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/MathieuCarriere/perslay" onclick="captureOutboundLink('https://github.com/MathieuCarriere/perslay'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> MathieuCarriere/perslay </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 2 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 80 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/arangoml/fastgraphml" onclick="captureOutboundLink('https://github.com/arangoml/fastgraphml'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> arangoml/fastgraphml </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 2 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 68 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/abhilash1910/SpectralEmbeddings" onclick="captureOutboundLink('https://github.com/abhilash1910/SpectralEmbeddings'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> abhilash1910/SpectralEmbeddings </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 2 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 65 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/shamim-hussain/egt" onclick="captureOutboundLink('https://github.com/shamim-hussain/egt'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> shamim-hussain/egt </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 2 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 51 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/zxhhh97/ABot" onclick="captureOutboundLink('https://github.com/zxhhh97/ABot'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> zxhhh97/ABot </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 2 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 18 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/fanzhenliu/dagad" onclick="captureOutboundLink('https://github.com/fanzhenliu/dagad'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> fanzhenliu/dagad </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 2 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 18 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="row task-library"> <div class="col-12 col-md-6"> <a href="https://github.com/nnaakkaaii/g2-MLP" onclick="captureOutboundLink('https://github.com/nnaakkaaii/g2-MLP'); return true;"> <div class="library-logo"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </div> nnaakkaaii/g2-MLP </a> </div> <div class="col-6 col-md-3"> <span class="task-library-pwc-count"> 2 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 10 <span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> </div> </div> </div> <div class="table-options"> <a href="#" id="libraries-see-less-trigger">Collapse 18</a> libraries. </div> </div> </div> </div>  <div class="title"> <h2 id="datasets">Datasets</h2> <hr> <div class="task-datasets"> <div class="col-md-12"> <ul class="list-unstyled"> <li> <a href="/dataset/cifar-10"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/52296e10-6143-483a-8eff-41b6f2a724e6.jpg"> CIFAR-10 </span> </a> </li> <li> <a href="/dataset/mnist"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000001-f66c5dc9_UOPLOsj.jpg"> MNIST </span> </a> </li> <li> <a href="/dataset/pubmed"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> Pubmed </span> </a> </li> <li> <a href="/dataset/ogb"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000005078-5b5b1cd9_oLn0Sj5.jpg"> OGB </span> </a> </li> <li> <a href="/dataset/reddit"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000001419-f6818660_49gvISv.jpg"> Reddit </span> </a> </li> <li> <a href="/dataset/cora"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000700-fc96b306_r4h6Zl5.jpg"> Cora </span> </a> </li> <li> <a href="/dataset/citeseer"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> Citeseer </span> </a> </li> <li> <a href="/dataset/proteins"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> PROTEINS </span> </a> </li> <li> <a href="/dataset/imdb-binary"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> IMDB-BINARY </span> </a> </li> <li> <a href="/dataset/mutag"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> MUTAG </span> </a> </li> <a href="/datasets?task=graph-classification"> <button class="dropdown-toggle badge badge-edit w-100 collapsed" type="button" > See all 52 graph classification datasets </button> </a> </ul> </div> </div> </div>  <div class="title"> <h2 id="subtasks">Subtasks</h2> <hr> <div class="task-subtasks"> 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187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/PetarV-/GAT" onclick="captureOutboundLink('https://github.com/PetarV-/GAT'); return true;" style="font-size:13px"> PetarV-/GAT </a> </span> • <span class="item-framework-link"> <img class="" src="data:image/png;base64,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" /> </span> • <span class="item-conference-link"> <a href="/conference/iclr-2018-1"> ICLR 2018 </a> </span> </p> <p class="item-strip-abstract">We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 92</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/graph-attention-networks" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/graph-attention-networks#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/semi-supervised-classification-with-graph"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1609.02907.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/semi-supervised-classification-with-graph">Semi-Supervised Classification with Graph Convolutional Networks</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/tkipf/pygcn" onclick="captureOutboundLink('https://github.com/tkipf/pygcn'); return true;" style="font-size:13px"> tkipf/pygcn </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="author-name-text item-date-pub">9 Sep 2016</span> </p> <p class="item-strip-abstract">We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 53</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/semi-supervised-classification-with-graph" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/semi-supervised-classification-with-graph#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/modeling-relational-data-with-graph"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1703.06103.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/modeling-relational-data-with-graph">Modeling Relational Data with Graph Convolutional Networks</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/tkipf/relational-gcn" onclick="captureOutboundLink('https://github.com/tkipf/relational-gcn'); return true;" style="font-size:13px"> tkipf/relational-gcn </a> </span> • <span class="item-framework-link"> <img class="" src="data:image/png;base64,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" /> </span> • <span class="author-name-text item-date-pub">17 Mar 2017</span> </p> <p class="item-strip-abstract">We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 27</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/modeling-relational-data-with-graph" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/modeling-relational-data-with-graph#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/imagenet-classification-with-deep"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/79567.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/imagenet-classification-with-deep">ImageNet Classification with Deep Convolutional Neural Networks</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://worksheets.codalab.org/worksheets/0xfafccca55b584e6eb1cf71979ad8e778" onclick="captureOutboundLink('https://worksheets.codalab.org/worksheets/0xfafccca55b584e6eb1cf71979ad8e778'); return true;" style="font-size:13px"> worksheets/0xfafccca5 </a> </span> • <span class="item-conference-link"> <a href="/conference/neurips-2012-12"> NeurIPS 2012 </a> </span> </p> <p class="item-strip-abstract">We trained a large, deep convolutional neural network to classify the 1. 3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 21</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/imagenet-classification-with-deep" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/imagenet-classification-with-deep#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/inductive-representation-learning-on-large"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1706.02216.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/inductive-representation-learning-on-large">Inductive Representation Learning on Large Graphs</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/williamleif/GraphSAGE" onclick="captureOutboundLink('https://github.com/williamleif/GraphSAGE'); return true;" style="font-size:13px"> williamleif/GraphSAGE </a> </span> • <span class="item-framework-link"> <img class="" src="data:image/png;base64,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" /> </span> • <span class="item-conference-link"> <a href="/conference/neurips-2017-12"> NeurIPS 2017 </a> </span> </p> <p class="item-strip-abstract">Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 20</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/inductive-representation-learning-on-large" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/inductive-representation-learning-on-large#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/how-powerful-are-graph-neural-networks"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1810.00826.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/how-powerful-are-graph-neural-networks">How Powerful are Graph Neural Networks?</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/weihua916/powerful-gnns" onclick="captureOutboundLink('https://github.com/weihua916/powerful-gnns'); return true;" style="font-size:13px"> weihua916/powerful-gnns </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="item-conference-link"> <a href="/conference/iclr-2019-5"> ICLR 2019 </a> </span> </p> <p class="item-strip-abstract">Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 19</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/how-powerful-are-graph-neural-networks" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/how-powerful-are-graph-neural-networks#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/lightgcn-simplifying-and-powering-graph"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2002.02126.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/lightgcn-simplifying-and-powering-graph">LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/gusye1234/pytorch-light-gcn" onclick="captureOutboundLink('https://github.com/gusye1234/pytorch-light-gcn'); return true;" style="font-size:13px"> gusye1234/pytorch-light-gcn </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="author-name-text item-date-pub">6 Feb 2020</span> </p> <p class="item-strip-abstract">We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 18</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/lightgcn-simplifying-and-powering-graph" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/lightgcn-simplifying-and-powering-graph#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/benchmarking-graph-neural-networks"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2003.00982.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/benchmarking-graph-neural-networks">Benchmarking Graph Neural Networks</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/graphdeeplearning/benchmarking-gnns" onclick="captureOutboundLink('https://github.com/graphdeeplearning/benchmarking-gnns'); return true;" style="font-size:13px"> graphdeeplearning/benchmarking-gnns </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="author-name-text item-date-pub">2 Mar 2020</span> </p> <p class="item-strip-abstract">In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 15</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/benchmarking-graph-neural-networks" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/benchmarking-graph-neural-networks#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/hierarchical-graph-representation-learning"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1806.08804.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/hierarchical-graph-representation-learning">Hierarchical Graph Representation Learning with Differentiable Pooling</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 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</span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="item-conference-link"> <a href="/conference/neurips-2018-12"> NeurIPS 2018 </a> </span> </p> <p class="item-strip-abstract">Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 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data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/dmlc/dgl/tree/master/examples/pytorch/ggnn" onclick="captureOutboundLink('https://github.com/dmlc/dgl/tree/master/examples/pytorch/ggnn'); return true;" style="font-size:13px"> dmlc/dgl </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="author-name-text item-date-pub">17 Nov 2015</span> </p> <p class="item-strip-abstract">Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 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