Graph Representation Learning

<!doctype html> <html lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <script> const GTAG_ENABLED = true ; const GTAG_TRACKING_ID = "UA-121182717-1"; const SENTRY_DSN_FRONTEND = "".trim(); const GLOBAL_CSRF_TOKEN = 'ImUcUGLXvqfpuVgrUOxpIrfiN2MOeAPBIXeGftBAP9MywHtZoES7jqV0LLOzwyHY'; const MEDIA_URL = "https://production-media.paperswithcode.com/"; const ASSETS_URL = "https://production-assets.paperswithcode.com"; run_after_frontend_loaded = window.run_after_frontend_loaded || []; </script> <link rel="preconnect" href="https://production-assets.paperswithcode.com"><link rel="dns-prefetch" href="https://production-assets.paperswithcode.com"><link rel="preload" as="font" type="font/woff2" href="https://production-assets.paperswithcode.com/perf/fonts/65e877e527022735c1a1.woff2" crossorigin><link rel="preload" as="font" type="font/woff2" href="https://production-assets.paperswithcode.com/perf/fonts/917632e36982ca7933c8.woff2" crossorigin><link rel="preload" as="font" type="font/woff2" href="https://production-assets.paperswithcode.com/perf/fonts/f1405bd8a987c2ea8a67.woff2" crossorigin><script>(()=>{if(GTAG_ENABLED){const t=document.createElement("script");function n(){window.dataLayer.push(arguments)}t.src=`https://www.googletagmanager.com/gtag/js?id=${GTAG_TRACKING_ID}`,document.head.appendChild(t),window.dataLayer=window.dataLayer||[],window.gtag=n,n("js",new Date),n("config",GTAG_TRACKING_ID),window.captureOutboundLink=function(t){n("event","click",{event_category:"outbound",event_label:t})}}else window.captureOutboundLink=function(n){document.location=n}})();</script><link rel="preload" as="script" href="https://production-assets.paperswithcode.com/perf/766.4af6b88b.js"><link rel="preload" as="script" href="https://production-assets.paperswithcode.com/perf/2.6da00df7.js"><link rel="preload" as="script" href="https://production-assets.paperswithcode.com/perf/351.a22a9607.js"><link rel="preload" as="script" href="https://production-assets.paperswithcode.com/perf/101.5f271f23.js"><link rel="preload" as="style" href="https://production-assets.paperswithcode.com/perf/918.c41196c3.css"><link rel="preload" as="style" href="https://production-assets.paperswithcode.com/perf/view_task.8e3945a3.css"><link rel="preload" as="script" href="https://production-assets.paperswithcode.com/perf/view_task.e61ab167.js"><link rel="stylesheet" href="https://production-assets.paperswithcode.com/perf/918.c41196c3.css"><link rel="stylesheet" href="https://production-assets.paperswithcode.com/perf/view_task.8e3945a3.css">  <title>Graph Representation Learning | Papers With Code</title> <meta name="description" content="The goal of **Graph Representation Learning** is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole. <span class="description-source">Source: [SIGN: Scalable Inception Graph Neural Networks ](https://arxiv.org/abs/2004.11198)</span>" />  <meta property="og:title" content="Papers with Code - Graph Representation Learning"> <meta property="og:description" content="The goal of **Graph Representation Learning** is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole. <span class="description-source">Source: [SIGN: Scalable Inception Graph Neural Networks ](https://arxiv.org/abs/2004.11198)</span>"> <meta property="og:image" content="https://raw.githubusercontent.com/hwwang55/GraphGAN/master/framework.jpg"> <meta property="og:url" content="https://paperswithcode.com/task/graph-representation-learning">  <meta name="twitter:card" content="summary_large_image"> <meta name="twitter:site" content="@paperswithcode"> <meta name="twitter:title" content="Papers with Code - Graph Representation Learning"> <meta name="twitter:description" content="The goal of **Graph Representation Learning** is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole. <span class="description-source">Source: [SIGN: Scalable Inception Graph Neural Networks ](https://arxiv.org/abs/2004.11198)</span>"> <meta name="twitter:creator" content="@paperswithcode"> <meta name="twitter:url" content="https://paperswithcode.com/task/graph-representation-learning"> <meta name="twitter:domain" content="paperswithcode.com">  <script type="application/ld+json">{ "@context": "http://schema.org", "@graph": { "@type": "CreativeWork", "@id": "graph-representation-learning", "name": "Graph Representation Learning", "description": "The goal of **Graph Representation Learning** is to construct a set of features (\u2018embeddings\u2019) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.\r\n\r\n\r\n\u003Cspan class=\"description-source\"\u003ESource: [SIGN: Scalable Inception Graph Neural Networks ](https://arxiv.org/abs/2004.11198)\u003C/span\u003E", "url": "https://paperswithcode.com/task/graph-representation-learning", "image": "https://raw.githubusercontent.com/hwwang55/GraphGAN/master/framework.jpg", "subjectOf": [ { "@type": "CreativeWork", "@id": "methodology", "name": "Methodology", "description": "Browse 201 tasks \u2022 989 datasets \u2022 766 ", "image": "https://paperswithcode.com/static/sota.jpeg", "headline": "Browse state-of-the-art in ML leaderboards \u2022 52008 papers with code." }, { "@type": "CreativeWork", "@id": "representation-learning", "name": "Representation Learning", "description": "**Representation Learning** is a process in machine learning where algorithms extract meaningful patterns from raw data to create representations that are easier to understand and process. These representations can be designed for interpretability, reveal hidden features, or be used for transfer learning. They are valuable across many fundamental machine learning tasks like [image classification](/task/image-classification) and [retrieval](/task/image-retrieval).\r\n\r\nDeep neural networks can be considered representation learning models that typically encode information which is projected into a different subspace. These representations are then usually passed on to a linear classifier to, for instance, train a classifier. \r\n\r\nRepresentation learning can be divided into:\r\n\r\n- **Supervised representation learning**: learning representations on task A using annotated data and used to solve task B\r\n- **Unsupervised representation learning**: learning representations on a task in an unsupervised way (label-free data). These are then used to address downstream tasks and reducing the need for annotated data when learning news tasks. Powerful models like [GPT](/method/gpt) and [BERT](/method/bert) leverage unsupervised representation learning to tackle language tasks. \r\n\r\nMore recently, [self-supervised learning (SSL)](/task/self-supervised-learning) is one of the main drivers behind unsupervised representation learning in fields like computer vision and NLP. \r\n\r\nHere are some additional readings to go deeper on the task:\r\n\r\n- [Representation Learning: A Review and New Perspectives](/paper/representation-learning-a-review-and-new) - Bengio et al. (2012)\r\n- [A Few Words on Representation Learning](https://sthalles.github.io/a-few-words-on-representation-learning/) - Thalles Silva\r\n\r\n\u003Cspan style=\"color:grey; opacity: 0.6\"\u003E( Image credit: [Visualizing and Understanding Convolutional Networks](https://arxiv.org/pdf/1311.2901.pdf) )\u003C/span\u003E", "image": "https://production-media.paperswithcode.com/tasks/representation-learning_Rkb0arA.jpeg", "subjectOf": [ { "@type": "CreativeWork", "@id": "methodology", "name": "Methodology", "description": "Browse 201 tasks \u2022 989 datasets \u2022 766 ", "image": "https://paperswithcode.com/static/sota.jpeg", "headline": "Browse state-of-the-art in ML leaderboards \u2022 52008 papers with code." } ], "headline": "Representation Learning" } ], "headline": "Graph Representation Learning" } }</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 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We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole. <span class="description-source">Source: [SIGN: Scalable Inception Graph Neural Networks ](https://arxiv.org/abs/2004.11198)</span></textarea> </div> </div> <div id="div_id_image" class="form-group"> <label for="id_image" class=""> Image </label> <div class=""> <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" id="addRow" role="dialog" aria-labelledby="addRowLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="addRowLabel">Add a new evaluation result row</h5> 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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>Methodology</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 Representation Learning</h1> <div class="artefact-information"> <p> 428 papers with code • 1 benchmarks • 6 datasets </p> </div> </div> <div class="col-lg-10">  <div class="description"> <div class="description-content"> <p>The goal of <strong>Graph Representation Learning</strong> is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.</p> <p><span class="description-source">Source: <a href="https://arxiv.org/abs/2004.11198">SIGN: Scalable Inception Graph Neural Networks </a></span></p> </div> </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 Representation Learning <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-representation-learning-on-coma';"> <td> <a href="/sota/graph-representation-learning-on-coma"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-representation-learning-on-coma-small_9b498c8a.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/graph-representation-learning-on-coma"> COMA </a> </div> </td> <td> <div class="black-links"> <a href="/sota/graph-representation-learning-on-coma"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> Pi-net-linear </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/-nets-deep-polynomial-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/-nets-deep-polynomial-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-representation-learning-on-coma" class="btn btn-primary">See all</a> </div> </td> </tr> </tbody> </table> </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 Representation Learning 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/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"> 3 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 383 <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/junxia97/simgrace" onclick="captureOutboundLink('https://github.com/junxia97/simgrace'); 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> junxia97/simgrace </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"> 77 <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/benedekrozemberczki/pytorch_geometric_temporal" onclick="captureOutboundLink('https://github.com/benedekrozemberczki/pytorch_geometric_temporal'); 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/pytorch_geometr… </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"> 2,678 <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/pbielak/graph-barlow-twins" onclick="captureOutboundLink('https://github.com/pbielak/graph-barlow-twins'); 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> pbielak/graph-barlow-twins </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"> 29 <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> <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/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"> 3 papers </span> </div> <div class="col-6 col-md-3"> <div class="library-stars text-nowrap"> 383 <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 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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> junxia97/simgrace </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"> 77 <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 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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/pytorch_geometr… </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"> 2,678 <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 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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> pbielak/graph-barlow-twins </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"> 29 <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> </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/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/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/reddit-binary"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> REDDIT-BINARY </span> </a> </li> <li> <a href="/dataset/coma"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000002904-1865eb2e.jpg"> COMA </span> </a> </li> <li> <a href="/dataset/wikigraphs"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000008096-9515efa6.jpg"> WikiGraphs </span> </a> </li> <li> <a href="/dataset/myket-android-application-install"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/bb0f57dc-9427-4b62-b577-7fa93ef851ee.jpg"> Myket Android Application Install </span> </a> </li> </ul> </div> </div> </div>  <div class="title"> <h2 id="subtasks">Subtasks</h2> <hr> <div class="task-subtasks"> <div class="col-md-12"> <ul class="list-unstyled"> <li> <a href="/task/knowledge-graph-embedding"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Knowledge Graph Embedding</span> </span> </a> </li> </ul> </div> </div> </div>  <div class="title paper-list" id="code"> <h2 id="papers-list" class="home-page-title">Most implemented papers</h2> <div class="paper-filter-btn"> <div class="btn-group" role="group"> <a data-title="Most implemented papers" data-call-url="/tasklist/graph-representation-learning/greatest" data-target="/task/graph-representation-learning" 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<span class="loader-ellips__dot"></span> <span class="loader-ellips__dot"></span> <span class="loader-ellips__dot"></span> <span class="loader-ellips__dot"></span> </div> </div> <div id="task-papers-list"> <div class="infinite-container text-center"> <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 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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/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 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/diffpool" onclick="captureOutboundLink('https://github.com/dmlc/dgl/tree/master/examples/pytorch/diffpool'); 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="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 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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/hierarchical-graph-representation-learning#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/evolvegcn-evolving-graph-convolutional"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1902.10191.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/evolvegcn-evolving-graph-convolutional">EvolveGCN: Evolving Graph Convolutional Networks for Dynamic 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/IBM/EvolveGCN" onclick="captureOutboundLink('https://github.com/IBM/EvolveGCN'); return true;" style="font-size:13px"> IBM/EvolveGCN </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">26 Feb 2019</span> </p> <p class="item-strip-abstract">Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.</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 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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/evolvegcn-evolving-graph-convolutional#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/graphsaint-graph-sampling-based-inductive"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1907.04931.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/graphsaint-graph-sampling-based-inductive">GraphSAINT: Graph Sampling Based Inductive Learning Method</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/GraphSAINT/GraphSAINT" onclick="captureOutboundLink('https://github.com/GraphSAINT/GraphSAINT'); return true;" style="font-size:13px"> GraphSAINT/GraphSAINT </a> </span> • <span class="item-framework-link"> <img class="" 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/> </span> • <span class="item-conference-link"> <a href="/conference/iclr-2020-1"> ICLR 2020 </a> </span> </p> <p class="item-strip-abstract">Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed 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> 7</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/graphsaint-graph-sampling-based-inductive" 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/graphsaint-graph-sampling-based-inductive#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/graphgan-graph-representation-learning-with"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1711.08267.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/graphgan-graph-representation-learning-with">GraphGAN: Graph Representation Learning with Generative Adversarial Nets</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/hwwang55/GraphGAN" onclick="captureOutboundLink('https://github.com/hwwang55/GraphGAN'); return true;" style="font-size:13px"> hwwang55/GraphGAN </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">22 Nov 2017</span> </p> <p class="item-strip-abstract">The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space.</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> 5</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/graphgan-graph-representation-learning-with" 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/graphgan-graph-representation-learning-with#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/fast-graph-representation-learning-with"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1903.02428.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/fast-graph-representation-learning-with">Fast Graph Representation Learning with PyTorch Geometric</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/rusty1s/pytorch_geometric" onclick="captureOutboundLink('https://github.com/rusty1s/pytorch_geometric'); return true;" style="font-size:13px"> rusty1s/pytorch_geometric </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 Mar 2019</span> </p> <p class="item-strip-abstract">We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.</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> 5</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/fast-graph-representation-learning-with" 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/fast-graph-representation-learning-with#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/sign-scalable-inception-graph-neural-networks"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2004.11198.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/sign-scalable-inception-graph-neural-networks">SIGN: Scalable Inception 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/twitter-research/sign" onclick="captureOutboundLink('https://github.com/twitter-research/sign'); return true;" style="font-size:13px"> twitter-research/sign </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">23 Apr 2020</span> </p> <p class="item-strip-abstract">Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media.</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> 5</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/sign-scalable-inception-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/sign-scalable-inception-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/qa-gnn-reasoning-with-language-models-and"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2104.06378.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/qa-gnn-reasoning-with-language-models-and">QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering</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 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<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/naacl-2021-4"> NAACL 2021 </a> </span> </p> <p class="item-strip-abstract">The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG.</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> 5</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/qa-gnn-reasoning-with-language-models-and" 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 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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/do-transformers-really-perform-bad-for-graph"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/social-images/JmpdeatDORochTCC.png');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/do-transformers-really-perform-bad-for-graph">Do Transformers Really Perform Bad for Graph Representation?</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/Microsoft/Graphormer" onclick="captureOutboundLink('https://github.com/Microsoft/Graphormer'); return true;" style="font-size:13px"> Microsoft/Graphormer </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 Jun 2021</span> </p> <p class="item-strip-abstract">Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.</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> 5</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/do-transformers-really-perform-bad-for-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/do-transformers-really-perform-bad-for-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/effect-of-choosing-loss-function-when-using-t"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2308.06862.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/effect-of-choosing-loss-function-when-using-t">Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic 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/erfanloghmani/myket-android-application-market-dataset" onclick="captureOutboundLink('https://github.com/erfanloghmani/myket-android-application-market-dataset'); return true;" style="font-size:13px"> erfanloghmani/myket-android-application-market-dataset </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">13 Aug 2023</span> </p> <p class="item-strip-abstract">These findings underscore the efficacy of the proposed loss functions in dynamic network modeling.</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 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