SIGN: Scalable Inception Graph Neural Networks

<!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 = 'BD2JUqAIxxJH4EEjbzqUYnHfnZh9HfJ006sv6RxqfEEghKLRhkxCiRfAkwys4JNg'; 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/351.a22a9607.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_paper.05773d2b.css"><link rel="stylesheet" href="https://production-assets.paperswithcode.com/perf/918.c41196c3.css"><link rel="stylesheet" href="https://production-assets.paperswithcode.com/perf/view_paper.05773d2b.css">  <title>SIGN: Scalable Inception Graph Neural Networks | Papers With Code</title> <meta name="description" content="5 code implementations in PyTorch. 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. The popularity of graph neural networks has sparked interest, both in academia and in industry, in developing methods that scale to very large graphs such as Facebook or Twitter social networks. In most of these approaches, the computational cost is alleviated by a sampling strategy retaining a subset of node neighbors or subgraphs at training time. In this paper we propose a new, efficient and scalable graph deep learning architecture which sidesteps the need for graph sampling by using graph convolutional filters of different size that are amenable to efficient precomputation, allowing extremely fast training and inference. Our architecture allows using different local graph operators (e.g. motif-induced adjacency matrices or Personalized Page Rank diffusion matrix) to best suit the task at hand. We conduct extensive experimental evaluation on various open benchmarks and show that our approach is competitive with other state-of-the-art architectures, while requiring a fraction of the training and inference time. Moreover, we obtain state-of-the-art results on ogbn-papers100M, the largest public graph dataset, with over 110 million nodes and 1.5 billion edges." />  <meta property="og:title" content="Papers with Code - SIGN: Scalable Inception Graph Neural Networks"> <meta property="og:description" content="5 code implementations in PyTorch. 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. The popularity of graph neural networks has sparked interest, both in academia and in industry, in developing methods that scale to very large graphs such as Facebook or Twitter social networks. In most of these approaches, the computational cost is alleviated by a sampling strategy retaining a subset of node neighbors or subgraphs at training time. In this paper we propose a new, efficient and scalable graph deep learning architecture which sidesteps the need for graph sampling by using graph convolutional filters of different size that are amenable to efficient precomputation, allowing extremely fast training and inference. Our architecture allows using different local graph operators (e.g. motif-induced adjacency matrices or Personalized Page Rank diffusion matrix) to best suit the task at hand. We conduct extensive experimental evaluation on various open benchmarks and show that our approach is competitive with other state-of-the-art architectures, while requiring a fraction of the training and inference time. Moreover, we obtain state-of-the-art results on ogbn-papers100M, the largest public graph dataset, with over 110 million nodes and 1.5 billion edges."> <meta property="og:image" content="https://production-media.paperswithcode.com/thumbnails/paper/2004.11198.jpg"> <meta property="og:url" content="https://paperswithcode.com/paper/sign-scalable-inception-graph-neural-networks">  <meta name="twitter:card" content="summary_large_image"> <meta name="twitter:site" content="@paperswithcode"> <meta name="twitter:title" content="Papers with Code - SIGN: Scalable Inception Graph Neural Networks"> <meta name="twitter:description" content="5 code implementations in PyTorch. 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. The popularity of graph neural networks has sparked interest, both in academia and in industry, in developing methods that scale to very large graphs such as Facebook or Twitter social networks. In most of these approaches, the computational cost is alleviated by a sampling strategy retaining a subset of node neighbors or subgraphs at training time. In this paper we propose a new, efficient and scalable graph deep learning architecture which sidesteps the need for graph sampling by using graph convolutional filters of different size that are amenable to efficient precomputation, allowing extremely fast training and inference. Our architecture allows using different local graph operators (e.g. motif-induced adjacency matrices or Personalized Page Rank diffusion matrix) to best suit the task at hand. We conduct extensive experimental evaluation on various open benchmarks and show that our approach is competitive with other state-of-the-art architectures, while requiring a fraction of the training and inference time. Moreover, we obtain state-of-the-art results on ogbn-papers100M, the largest public graph dataset, with over 110 million nodes and 1.5 billion edges."> <meta name="twitter:creator" content="@paperswithcode"> <meta name="twitter:url" content="https://paperswithcode.com/paper/sign-scalable-inception-graph-neural-networks"> <meta name="twitter:domain" content="paperswithcode.com">  <script type="application/ld+json">{ "@context": "http://schema.org", "@graph": { "@type": "ScholarlyArticle", "@id": "2004.11198", "name": "SIGN: Scalable Inception Graph Neural Networks", "description": "5 code implementations in PyTorch. 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. The popularity of graph neural networks has sparked interest, both in academia and in industry, in developing methods that scale to very large graphs such as Facebook or Twitter social networks. In most of these approaches, the computational cost is alleviated by a sampling strategy retaining a subset of node neighbors or subgraphs at training time. In this paper we propose a new, efficient and scalable graph deep learning architecture which sidesteps the need for graph sampling by using graph convolutional filters of different size that are amenable to efficient precomputation, allowing extremely fast training and inference. Our architecture allows using different local graph operators (e.g. motif-induced adjacency matrices or Personalized Page Rank diffusion matrix) to best suit the task at hand. We conduct extensive experimental evaluation on various open benchmarks and show that our approach is competitive with other state-of-the-art architectures, while requiring a fraction of the training and inference time. Moreover, we obtain state-of-the-art results on ogbn-papers100M, the largest public graph dataset, with over 110 million nodes and 1.5 billion edges.", "url": "https://paperswithcode.com/paper/sign-scalable-inception-graph-neural-networks", "image": "https://production-media.paperswithcode.com/thumbnails/paper/2004.11198.jpg", "headline": "SIGN: Scalable Inception Graph Neural Networks", "abstract": "5 code implementations in PyTorch. 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. The popularity of graph neural networks has sparked interest, both in academia and in industry, in developing methods that scale to very large graphs such as Facebook or Twitter social networks. In most of these approaches, the computational cost is alleviated by a sampling strategy retaining a subset of node neighbors or subgraphs at training time. In this paper we propose a new, efficient and scalable graph deep learning architecture which sidesteps the need for graph sampling by using graph convolutional filters of different size that are amenable to efficient precomputation, allowing extremely fast training and inference. Our architecture allows using different local graph operators (e.g. motif-induced adjacency matrices or Personalized Page Rank diffusion matrix) to best suit the task at hand. We conduct extensive experimental evaluation on various open benchmarks and show that our approach is competitive with other state-of-the-art architectures, while requiring a fraction of the training and inference time. 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value="Paper" /> <div class="form-group"> <label>Description</label><br /> <div class="form-check form-check-inline"> <input id="description-mode-default" class="form-check-input display-toggle-switch" checked type="radio" name="description-mode" value="default" data-target="display-description-default" /> <label class="form-check-label" for="description-mode-default">Default</label> </div> <div class="form-check form-check-inline"> <input id="description-mode-custom" class="form-check-input display-toggle-switch" type="radio" name="description-mode" value="custom" data-target="display-description-custom" /> <label class="form-check-label" for="description-mode-custom">Custom</label> </div> </div> <div class="form-group"> <div id="display-description-default" data-name="description-mode"> <textarea class="form-control" rows="3" readonly >5 code implementations in PyTorch. 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. The popularity of graph neural networks has sparked interest, both in academia and in industry, in developing methods that scale to very large graphs such as Facebook or Twitter social networks. In most of these approaches, the computational cost is alleviated by a sampling strategy retaining a subset of node neighbors or subgraphs at training time. In this paper we propose a new, efficient and scalable graph deep learning architecture which sidesteps the need for graph sampling by using graph convolutional filters of different size that are amenable to efficient precomputation, allowing extremely fast training and inference. Our architecture allows using different local graph operators (e.g. motif-induced adjacency matrices or Personalized Page Rank diffusion matrix) to best suit the task at hand. We conduct extensive experimental evaluation on various open benchmarks and show that our approach is competitive with other state-of-the-art architectures, while requiring a fraction of the training and inference time. Moreover, we obtain state-of-the-art results on ogbn-papers100M, the largest public graph dataset, with over 110 million nodes and 1.5 billion edges.</textarea> </div> <div id="display-description-custom" data-name="description-mode"> <textarea class="form-control" id="description-input" rows="3" ></textarea> </div> </div> <div class="form-group"> <label>Image</label><br /> <div class="form-group"> <div class="form-check form-check-inline"> <input id="image-mode-default" class="form-check-input display-toggle-switch" checked type="radio" name="image-mode" value="default" data-target="display-image-default" /> <label class="form-check-label" for="image-mode-default">Default</label> </div> <div class="form-check form-check-inline"> <input id="image-mode-custom" class="form-check-input display-toggle-switch" type="radio" name="image-mode" value="custom" data-target="display-image-custom" /> <label class="form-check-label" for="image-mode-custom">Custom</label> </div> <div 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<th>MODEL</th> <th>METRIC NAME</th> <th>METRIC VALUE</th> <th>GLOBAL RANK</th> <th>REMOVE</th> </tr> <tr> <td> Node Classification </td> <td> AMZ Comp </td> <td> SIGN </td> <td> Accuracy </td> <td> 85.93 ± 1.21 </td> <td> # 5 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="15936"> <input type="hidden" name="remove_metric_pk" value="2351"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Classification </td> <td> AMZ Photo </td> <td> SIGN </td> <td> Accuracy </td> <td> 91.72 ± 1.20 </td> <td> # 13 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="15934"> <input type="hidden" name="remove_metric_pk" value="2352"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Classification </td> <td> Coauthor CS </td> <td> SIGN </td> <td> Accuracy </td> <td> 91.98 ± 0.50 </td> <td> # 17 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="15935"> <input type="hidden" name="remove_metric_pk" value="2350"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-arxiv </td> <td> SIGN </td> <td> Test Accuracy </td> <td> 0.7195 ± 0.0011 </td> <td> # 69 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="36003"> <input type="hidden" name="remove_metric_pk" value="25353"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-arxiv </td> <td> SIGN </td> <td> Validation Accuracy </td> <td> 0.7323 ± 0.0006 </td> <td> # 65 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="36003"> <input type="hidden" name="remove_metric_pk" value="25354"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-arxiv </td> <td> SIGN </td> <td> Number of params </td> <td> 3566128 </td> <td> # 12 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="36003"> <input type="hidden" name="remove_metric_pk" value="26372"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-arxiv </td> <td> SIGN </td> <td> Ext. data </td> <td> No </td> <td> # 1 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="36003"> <input type="hidden" name="remove_metric_pk" value="37401"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-mag </td> <td> SIGN </td> <td> Test Accuracy </td> <td> 0.4046 ± 0.0012 </td> <td> # 32 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="107014"> <input type="hidden" name="remove_metric_pk" value="25359"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-mag </td> <td> SIGN </td> <td> Validation Accuracy </td> <td> 0.4068 ± 0.0010 </td> <td> # 33 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="107014"> <input type="hidden" name="remove_metric_pk" value="25360"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-mag </td> <td> SIGN </td> <td> Number of params </td> <td> 3724645 </td> <td> # 35 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="107014"> <input type="hidden" name="remove_metric_pk" value="26374"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-mag </td> <td> SIGN </td> <td> Ext. data </td> <td> No </td> <td> # 1 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="107014"> <input type="hidden" name="remove_metric_pk" value="37403"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-papers100M </td> <td> SIGN </td> <td> Test Accuracy </td> <td> 0.6568 ± 0.0006 </td> <td> # 17 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="36019"> <input type="hidden" name="remove_metric_pk" value="25356"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-papers100M </td> <td> SIGN </td> <td> Validation Accuracy </td> <td> 0.6932 ± 0.0006 </td> <td> # 17 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="36019"> <input type="hidden" name="remove_metric_pk" value="25357"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-papers100M </td> <td> SIGN </td> <td> Number of params </td> <td> 1008812 </td> <td> # 16 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="36019"> <input type="hidden" name="remove_metric_pk" value="26373"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-papers100M </td> <td> SIGN </td> <td> Ext. data </td> <td> No </td> <td> # 1 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="36019"> <input type="hidden" name="remove_metric_pk" value="37402"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-papers100M </td> <td> SIGN-XL </td> <td> Test Accuracy </td> <td> 0.6606 ± 0.0019 </td> <td> # 15 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="36018"> <input type="hidden" name="remove_metric_pk" value="25356"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-papers100M </td> <td> SIGN-XL </td> <td> Validation Accuracy </td> <td> 0.6984 ± 0.0006 </td> <td> # 15 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="36018"> <input type="hidden" name="remove_metric_pk" value="25357"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-papers100M </td> <td> SIGN-XL </td> <td> Number of params </td> <td> 7180460 </td> <td> # 12 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="36018"> <input type="hidden" name="remove_metric_pk" value="26373"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-papers100M </td> <td> SIGN-XL </td> <td> Ext. data </td> <td> No </td> <td> # 1 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="36018"> <input type="hidden" name="remove_metric_pk" value="37402"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-products </td> <td> SIGN </td> <td> Test Accuracy </td> <td> 0.8052 ± 0.0016 </td> <td> # 43 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="35932"> <input type="hidden" name="remove_metric_pk" value="25347"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-products </td> <td> SIGN </td> <td> Validation Accuracy </td> <td> 0.9299 ± 0.0004 </td> <td> # 24 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="35932"> <input type="hidden" name="remove_metric_pk" value="25348"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-products </td> <td> SIGN </td> <td> Number of params </td> <td> 3483703 </td> <td> # 8 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="35932"> <input type="hidden" name="remove_metric_pk" value="26370"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Property Prediction </td> <td> ogbn-products </td> <td> SIGN </td> <td> Ext. data </td> <td> No </td> <td> # 1 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="35932"> <input type="hidden" name="remove_metric_pk" value="37399"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Classification </td> <td> PPI </td> <td> SIGN </td> <td> F1 </td> <td> 96.50 </td> <td> # 17 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="15933"> <input type="hidden" name="remove_metric_pk" value="1307"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> <tr> <td> Node Classification </td> <td> Reddit </td> <td> SIGN </td> <td> Accuracy </td> <td> 96.60% </td> <td> # 8 </td> <td> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_row_pk" value="15932"> <input type="hidden" name="remove_metric_pk" value="2387"> <button type="submit" class="btn btn-danger"> - </button> </form> </td> </tr> </table> </div> </div> </form> </div> </div> </div>  <div class="modal fade" id="addTask" role="dialog" aria-labelledby="addTaskLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="addTaskLabel">Add a task</h5> <button type="button" class="close btn-close" data-bs-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="current-tasks-title">Attached tasks:</div> <ul class="list-unstyled"> <li> <a href="/task/graph-representation-learning"> <span class="badge badge-primary">GRAPH REPRESENTATION LEARNING</span> </a> </li> </ul> <ul class="list-unstyled"> <li> <a href="/task/graph-sampling"> <span class="badge badge-primary">GRAPH SAMPLING</span> </a> </li> </ul> <ul class="list-unstyled"> <li> <a href="/task/node-classification"> <span class="badge badge-primary">NODE CLASSIFICATION</span> </a> </li> </ul> <ul class="list-unstyled"> <li> <a href="/task/node-property-prediction"> <span class="badge badge-primary">NODE PROPERTY PREDICTION</span> </a> </li> </ul> <ul class="list-unstyled"> <li> <a href="/task/representation-learning"> <span class="badge badge-primary">REPRESENTATION LEARNING</span> </a> </li> </ul> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <div id="div_id_task" class="form-group"> <label for="id_task" class=""> Add: </label> <div class=""> <select name="task" class="modelselect2 form-control" id="id_task" data-autocomplete-light-language="en" data-autocomplete-light-url="/task-autocomplete/" data-autocomplete-light-function="select2"> <option value="" selected>---------</option> </select> </div> </div> <div class="modal-help-text"> Not in the list?<br/> <a href="#" id="new-task-form-toggle"> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Create a new task</a>. </div> <div id="new-task-form" style="display: none"> <div id="div_id_new_task_name" class="form-group"> <label for="id_new_task_name" class=""> New task name: </label> <div class=""> <input type="text" name="new_task_name" maxlength="200" class="textinput textInput form-control" id="id_new_task_name"> </div> </div> <div id="div_id_new_task_area" class="form-group"> <label for="id_new_task_area" class=""> Top-level area: </label> <div class=""> <select name="new_task_area" class="select form-control" id="id_new_task_area"> <option value="" selected>---------</option> <option value="17">Adversarial</option> <option value="18">Audio</option> <option value="11">Computer Code</option> <option value="3">Computer Vision</option> <option value="9">Graphs</option> <option value="15">Knowledge Base</option> <option value="7">Medical</option> <option value="6">Methodology</option> <option value="5">Miscellaneous</option> <option value="12">Music</option> <option value="4">Natural Language Processing</option> <option value="13">Playing Games</option> <option value="14">Reasoning</option> <option value="16">Robots</option> <option value="10">Speech</option> <option value="8">Time Series</option> </select> </div> </div> <div id="div_id_new_task_parent" class="form-group"> <label for="id_new_task_parent" class=""> Parent task (if any): </label> <div class=""> <select name="new_task_parent" class="modelselect2 form-control" id="id_new_task_parent" data-autocomplete-light-language="en" data-autocomplete-light-url="/task-and-tag-autocomplete/" data-autocomplete-light-function="select2"> <option value="" selected>---------</option> </select> </div> </div> <div id="div_id_new_task_desc" class="form-group"> <label for="id_new_task_desc" class=""> Description: </label> <div class=""> <textarea name="new_task_desc" cols="40" rows="3" class="textarea form-control" id="id_new_task_desc"> </textarea> </div> </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="removeTask" tabindex="-1" role="dialog" aria-labelledby="removeTaskLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="removeTaskLabel">Remove a task</h5> <button type="button" class="close btn-close" data-bs-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <form action="" method="post"> <div class="modal-body"> <ul class="list-unstyled paper-tasks"> <form action="" method="post"> <li> <a href="/task/graph-representation-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Graph Representation Learning</span> </span> </a> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_task_pk" value="227"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled paper-tasks"> <form action="" method="post"> <li> <a href="/task/graph-sampling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Graph Sampling</span> </span> </a> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_task_pk" value="2312"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled paper-tasks"> <form action="" method="post"> <li> <a href="/task/node-classification"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000407-3df5d6f0.jpg"> <span>Node Classification</span> </span> </a> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_task_pk" value="407"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled paper-tasks"> <form action="" method="post"> <li> <a href="/task/node-property-prediction"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Node Property Prediction</span> </span> </a> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_task_pk" value="2832"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled paper-tasks"> <form action="" method="post"> <li> <a href="/task/representation-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000228-3131cfbf_nx72Tly.jpg"> <span>Representation Learning</span> </span> </a> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <input type="hidden" name="remove_task_pk" value="228"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> </div> </form> </div> </div> </div>  <div class="modal fade" id="addMethod" role="dialog" aria-labelledby="addMethodLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="addMethodLabel">Add a method</h5> <button type="button" class="close btn-close" data-bs-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="current-methods-title">Attached methods:</div> <ul class="list-unstyled"> <li> <a href="/method/1x1-convolution"> <span class="badge badge-primary">1X1 CONVOLUTION</span> </a> </li> </ul> <ul class="list-unstyled"> <li> <a href="/method/convolution"> <span class="badge badge-primary">CONVOLUTION</span> </a> </li> </ul> <ul class="list-unstyled"> <li> <a href="/method/inception-module"> <span class="badge badge-primary">INCEPTION MODULE</span> </a> </li> </ul> <ul class="list-unstyled"> <li> <a href="/method/max-pooling"> <span class="badge badge-primary">MAX POOLING</span> </a> </li> </ul> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <div id="div_id_method" class="form-group"> <label for="id_method" class=""> Add: </label> <div class=""> <select name="method" class="modelselect2 form-control" id="id_method" data-autocomplete-light-language="en" data-autocomplete-light-url="/method-autocomplete/" data-autocomplete-light-function="select2"> <option value="" selected>---------</option> </select> </div> </div> <div class="modal-help-text"> Not in the list?<br/> <a href="#" id="new-method-form-toggle"> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Create a new method</a>. </div> <div id="new-method-form" style="display: none"> <div id="div_id_new_method_name" class="form-group"> <label for="id_new_method_name" class=""> <b>New method name</b> (e.g. ReLU): </label> <div class=""> <input type="text" name="new_method_name" maxlength="200" minlength="2" class="textinput textInput form-control" id="id_new_method_name"> </div> </div> <div id="div_id_new_method_full_name" class="form-group"> <label for="id_new_method_full_name" class=""> <b>New method full name</b> (e.g. Rectified Linear Unit): </label> <div class=""> <input type="text" name="new_method_full_name" maxlength="200" minlength="2" class="textinput textInput form-control" id="id_new_method_full_name"> </div> </div> <div id="div_id_new_method_paper" class="form-group"> <label for="id_new_method_paper" class=""> <b>Paper where method was first introduced</b>: </label> <div class=""> <select name="new_method_paper" class="modelselect2 form-control" id="id_new_method_paper" data-autocomplete-light-language="en" data-autocomplete-light-url="/paper-autocomplete/" data-autocomplete-light-function="select2"> <option value="" selected>---------</option> </select> </div> </div> <div id="div_id_new_method_collection" class="form-group"> <label for="id_new_method_collection" class=""> <b>Method category</b> (e.g. Activation Functions): <i>If no match, add something for now then you can add a new category afterwards.</i> </label> <div class=""> <select name="new_method_collection" class="modelselect2 form-control" id="id_new_method_collection" data-autocomplete-light-language="en" data-autocomplete-light-url="/method-collection-autocomplete/" data-autocomplete-light-function="select2"> <option value="" selected>---------</option> </select> </div> </div> <div id="div_id_new_method_desc" class="form-group"> <label 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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> Badge </th> <th style="width: 50%">Markdown </th> </tr> <tr> <td> <img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-classification-on-amz-comp" /> </td> <td> <code>[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-classification-on-amz-comp)](https://paperswithcode.com/sota/node-classification-on-amz-comp?p=sign-scalable-inception-graph-neural-networks)</code> </td> </tr> <tr> <td> <img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-classification-on-reddit" /> </td> <td> <code>[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-classification-on-reddit)](https://paperswithcode.com/sota/node-classification-on-reddit?p=sign-scalable-inception-graph-neural-networks)</code> </td> </tr> <tr> <td> <img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-classification-on-amz-photo" /> </td> <td> <code>[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-classification-on-amz-photo)](https://paperswithcode.com/sota/node-classification-on-amz-photo?p=sign-scalable-inception-graph-neural-networks)</code> </td> </tr> <tr> <td> <img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-property-prediction-on-ogbn-papers100m" /> </td> <td> <code>[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-property-prediction-on-ogbn-papers100m)](https://paperswithcode.com/sota/node-property-prediction-on-ogbn-papers100m?p=sign-scalable-inception-graph-neural-networks)</code> </td> </tr> <tr> <td> <img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-classification-on-coauthor-cs" /> </td> <td> <code>[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-classification-on-coauthor-cs)](https://paperswithcode.com/sota/node-classification-on-coauthor-cs?p=sign-scalable-inception-graph-neural-networks)</code> </td> </tr> <tr> <td> <img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-classification-on-ppi" /> </td> <td> <code>[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-classification-on-ppi)](https://paperswithcode.com/sota/node-classification-on-ppi?p=sign-scalable-inception-graph-neural-networks)</code> </td> </tr> <tr> <td> <img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-property-prediction-on-ogbn-mag" /> </td> <td> <code>[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-property-prediction-on-ogbn-mag)](https://paperswithcode.com/sota/node-property-prediction-on-ogbn-mag?p=sign-scalable-inception-graph-neural-networks)</code> </td> </tr> <tr> <td> <img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-property-prediction-on-ogbn-products" /> </td> <td> <code>[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-property-prediction-on-ogbn-products)](https://paperswithcode.com/sota/node-property-prediction-on-ogbn-products?p=sign-scalable-inception-graph-neural-networks)</code> </td> </tr> <tr> <td> <img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-property-prediction-on-ogbn-arxiv" /> </td> <td> <code>[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/sign-scalable-inception-graph-neural-networks/node-property-prediction-on-ogbn-arxiv)](https://paperswithcode.com/sota/node-property-prediction-on-ogbn-arxiv?p=sign-scalable-inception-graph-neural-networks)</code> </td> </tr> </table> </div> </div> </div> </div> </div> </div> </div>  <div class="modal fade" id="editDatasets" role="dialog" aria-labelledby="editDatasetsLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="editDatasetsLabel">Edit Datasets</h5> <button type="button" class="close btn-close" data-bs-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <form action="" method="post"> <div class="modal-body paper-page-edit-dataset-modal"> <input type="hidden" name="csrfmiddlewaretoken" value="o6GW1S9afYIQ2Wa4dOIVBnAhzR10ft9dNz6Idj6SX5Dpf2hCjzPDVR8CwoijCXdt"> <div id="div_id_introduced" class="form-group"> <label for="id_introduced" class=""> Add or remove datasets <b>introduced</b> in this paper: </label> <div class=""> <select name="introduced" data-container-css-class="" data-allow-clear="false" style="width: 100%" class="modelselect2multiple form-control" id="id_introduced" data-autocomplete-light-language="en" data-autocomplete-light-url="/dataset-autocomplete/" data-autocomplete-light-function="select2" multiple> </select><div style="display:none" class="dal-forward-conf" id="dal-forward-conf-for_id_introduced"><script type="text/dal-forward-conf">[{"type": "const", "val": true, "dst": "canonical_only"}, {"type": "const", "val": true, "dst": "disable_create_option"}]</script></div> </div> </div> <div id="div_id_used" class="form-group"> <label for="id_used" class=""> Add or remove other datasets <b>used</b> in this paper: </label> <div class=""> <select name="used" data-container-css-class="" data-allow-clear="false" style="width: 100%" class="modelselect2multiple form-control" id="id_used" data-autocomplete-light-language="en" data-autocomplete-light-url="/dataset-autocomplete/" data-autocomplete-light-function="select2" multiple> <option value="5078" selected>OGB</option> <option value="901" selected>PPI</option> <option value="1419" selected>Reddit</option> </select><div style="display:none" class="dal-forward-conf" id="dal-forward-conf-for_id_used"><script type="text/dal-forward-conf">[{"type": "const", "val": true, "dst": "canonical_only"}, {"type": "const", "val": true, "dst": "disable_create_option"}]</script></div> </div> </div> <div style="display: inline-block; padding-bottom: 15px;font-size:14px;"> Paper introduces a new dataset? <div style="padding-top:4px"> <a href="/contribute/dataset/new"> <span class=" icon-wrapper icon-fa icon-fa-solid" data-name="plus-circle"><svg viewBox="0 0 512 514.999" xmlns="http://www.w3.org/2000/svg"><path d="M256 9.998c137 0 248 111 248 248s-111 248-248 248-248-111-248-248 111-248 248-248zm144 276v-56c0-6.6-5.4-12-12-12h-92v-92c0-6.6-5.4-12-12-12h-56c-6.6 0-12 5.4-12 12v92h-92c-6.6 0-12 5.4-12 12v56c0 6.6 5.4 12 12 12h92v92c0 6.6 5.4 12 12 12h56c6.6 0 12-5.4 12-12v-92h92c6.6 0 12-5.4 12-12z"/></svg></span> Add a new dataset here </a> </div> </div> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary" name="edit-datasets"> Save </button> </div> </form> </div> </div> </div> </template> <div class="container content content-buffer "> <main> <div class="paper-title"> <div class="row"> <div class="col-md-12"> <h1> SIGN: Scalable Inception Graph Neural Networks </h1> <div class="authors"> <p> <span class="author-span">23 Apr 2020</span>  ·  <span class="author-span"> <a href="/author/fabrizio-frasca">Fabrizio Frasca</a></span>, <span class="author-span"> <a href="/author/emanuele-rossi">Emanuele Rossi</a></span>, <span class="author-span"> <a href="/author/davide-eynard">Davide Eynard</a></span>, <span class="author-span"> <a href="/author/ben-chamberlain">Ben Chamberlain</a></span>, <span class="author-span"> <a href="/author/michael-bronstein">Michael Bronstein</a></span>, <span class="author-span"> <a href="/author/federico-monti">Federico Monti</a></span> <span class="hidden-element">· </span><button type="button" class="badge-edit" data-bs-toggle="modal" data-bs-toggle="modal" data-bs-target="#loginModal"> <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> <span>Edit social preview</span> </button> </p> </div> </div> </div> </div> <div class="paper-abstract"> <div class="row"> <div class="col-md-12"> <p> 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. The popularity of graph neural networks has sparked interest, both in academia and in industry, in developing methods that scale to very large graphs such as Facebook or Twitter social networks. In most of these approaches, the computational cost is alleviated by a sampling strategy retaining a subset of node neighbors or subgraphs at training time. In this paper we propose a new, efficient and scalable graph deep learning architecture which sidesteps the need for graph sampling by using graph convolutional filters of different size that are amenable to efficient precomputation, allowing extremely fast training and inference. Our architecture allows using different local graph operators (e.g. motif-induced adjacency matrices or Personalized Page Rank diffusion matrix) to best suit the task at hand. We conduct extensive experimental evaluation on various open benchmarks and show that our approach is competitive with other state-of-the-art architectures, while requiring a fraction of the training and inference time. Moreover, we obtain state-of-the-art results on ogbn-papers100M, the largest public graph dataset, with over 110 million nodes and 1.5 billion edges. </p> <a href="https://arxiv.org/pdf/2004.11198v3.pdf" onclick="captureOutboundLink('https://arxiv.org/pdf/2004.11198v3.pdf'); return true;" class="badge badge-light "> <span class=" icon-wrapper icon-fa icon-fa-regular" data-name="file-pdf"><svg viewBox="0 0 384 513.795" xmlns="http://www.w3.org/2000/svg"><path d="M369.9 98.88c9 9 14.1 21.3 14.1 34v332.1c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48v-416c0-26.5 21.5-48 48-48.1h204.1c12.7 0 24.9 5.1 33.9 14.1zm-37.8 30.1L256 52.88v76.1h76.1zM48 464.98h288v-288H232c-13.3 0-24-10.7-24-24v-104H48v416zm250.2-143.7c10.5 10.5 8 38.7-17.5 38.7-14.8 0-36.9-6.8-55.8-17-21.6 3.6-46 12.7-68.4 20.1-50.1 86.4-79.4 47-76.1 31.2 4-20 31-35.9 51-46.2 10.5-18.4 25.4-50.5 35.4-74.4-7.4-28.6-11.4-51-7-67.1 4.8-17.7 38.4-20.3 42.6 5.9 4.7 15.4-1.5 39.9-5.4 56 8.1 21.3 19.6 35.8 36.8 46.3 17.4-2.2 52.2-5.5 64.4 6.5zm-198.1 77.8c0 .7 11.4-4.7 30.4-35-5.9 5.5-25.299 21.3-30.4 35zm81.6-190.6c-2.5 0-2.6 26.9 1.8 40.8 4.9-8.7 5.6-40.8-1.8-40.8zm-24.4 136.6c15.9-6.1 34-14.9 54.8-19.2-11.199-8.3-21.8-20.4-30.1-35.5-6.7 17.7-15 37.8-24.7 54.7zm131.6-5c3.6-2.4-2.2-10.4-37.3-7.8 32.3 13.8 37.3 7.8 37.3 7.8z"/></svg></span> <span>PDF</span> </a> <a href="https://arxiv.org/abs/2004.11198v3" onclick="captureOutboundLink('https://arxiv.org/abs/2004.11198v3'); return true;" class="badge badge-light "> <span class=" icon-wrapper icon-fa icon-fa-regular" data-name="file"><svg viewBox="0 0 384 513.795" xmlns="http://www.w3.org/2000/svg"><path d="M369.9 98.88c9 9 14.1 21.3 14.1 34v332.1c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48v-416c0-26.5 21.5-48 48-48.1h204.1c12.7 0 24.9 5.1 33.9 14.1zm-37.8 30.1L256 52.88v76.1h76.1zM48 464.98h288v-288H232c-13.3 0-24-10.7-24-24v-104H48v416z"/></svg></span> <span>Abstract</span> </a> </div> </div> </div> <div class="row"> <div class="col-md-7 paper-section-first" 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<hr/> </div> </div> <div class="paper-tasks"> <div class="row"> <div class="col-md-12"> <a href="/task/graph-representation-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Graph Representation Learning</span> </span> </a> <a href="/task/graph-sampling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Graph Sampling</span> </span> </a> <a href="/task/node-classification"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000407-3df5d6f0.jpg"> <span>Node Classification</span> </span> </a> <a href="/task/node-property-prediction"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Node Property Prediction</span> </span> </a> <a href="/task/representation-learning"> <span class="badge badge-primary"> <img 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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 </button> </div> </div> </h2> <hr/> </div> </div> <div class="paper-datasets"> <div class="row"> <div class="col-md-12"> <span class="badge badge-primary"> <a href="/dataset/ogb"> <img class="dataset-list-image" src="https://production-media.paperswithcode.com/thumbnails/dataset-small/dataset-0000005078-ce502adf.jpg"> OGB </a> </span> <span class="badge badge-primary"> <a href="/dataset/reddit"> <img class="dataset-list-image" src="https://production-media.paperswithcode.com/thumbnails/dataset-small/dataset-0000001419-c5cbb36a.jpg"> Reddit </a> </span> <span class="badge badge-primary"> <a href="/dataset/ppi"> <span class="icon-list-image" style="opacity:0.4;color:#A59F78"><span class=" icon-wrapper icon-fa icon-fa-solid" data-name="file"><svg viewBox="0 0 384 514.999" xmlns="http://www.w3.org/2000/svg"><path d="M224 137.998c0 13.2 10.8 24 24 24h136v328c0 13.3-10.7 24-24 24H24c-13.3 0-24-10.7-24-24v-464c0-13.3 10.7-24 24-24h200v136zm160-14.1v6.1H256v-128h6.1c6.4 0 12.5 2.5 17 7l97.9 98c4.5 4.5 7 10.6 7 16.9z"/></svg></span></span> PPI </a> </span> </div> </div> </div> </div> </div> </div>  <div class="row"> <div id="results" class="col-md-12 paper-evaluation-section-title"> <div class="paper-section-title"> <div class="row"> <div class="col-md-12 zero-padding"> <h2>Results from the Paper <div class="float-right"> <div class="edit-button"> <a class="dropdown-toggle badge badge-edit" id="evalEditMenu" href="/paper/sign-scalable-inception-graph-neural-networks/review/"> <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 </a> </div> </div> </h2> <hr/> <div class="paper-evaluation-badge"> <div class="sota"> <p> <a href="/sota/node-classification-on-amz-comp"> <img style="height:20px;width:35px;position:relative;top:1px;" src="https://production-media.paperswithcode.com/sota-thumbs/node-classification-on-amz-comp-small_86ad8fb7.png"/> </a> Ranked #5 on <a href="/sota/node-classification-on-amz-comp"> Node Classification on AMZ Comp </a> </p> </div>      <a href="#" class="get-badge-button float-right" data-bs-toggle="modal" data-bs-target="#badgeModal"> <span class=" icon-wrapper icon-fa icon-fa-light" data-name="arrow-to-right"><svg viewBox="0 0 448 520.146" xmlns="http://www.w3.org/2000/svg"><path d="M215 100.5c4.7-4.7 12.3-4.7 17 0l148.6 148c4.7 4.7 4.7 12.3 0 17l-148.5 148c-4.7 4.7-12.3 4.7-17 0l-7.1-7.1c-4.7-4.7-4.7-12.3 0-17L323.9 274H12c-6.6 0-12-5.4-12-12v-10c0-6.6 5.4-12 12-12h311.9l-116-115.4a12.01 12.01 0 0 1 0-17zM448 77v360c0 6.6-5.4 12-12 12h-8c-6.6 0-12-5.4-12-12V77c0-6.6 5.4-12 12-12h8c6.6 0 12 5.4 12 12z"/></svg></span> Get a GitHub badge </a> </div> </div> </div> </div> </div> </div> <div class="paper-evaluation-section" id="evaluation"> <div class="row"> <div class="col-md-12"> <div class="sota-table table-responsive"> <table class="table-striped"> <tr> <th>Task</th> <th>Dataset</th> <th>Model</th> <th>Metric Name</th> <th>Metric Value</th> <th>Global Rank</th> <th>Result</th> <th>Benchmark</th> </tr> <tr> <td rowspan="1" class="rowspan-td"> Node Classification </td> <td rowspan="1" class="rowspan-td"> AMZ Comp </td> <td rowspan="1" class="rowspan-td model-col"> SIGN </td> <td style="vertical-align: top; padding-top: 18px;"> Accuracy </td> <td> 85.93 ± 1.21 </td> <td> # 5 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=15936"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-classification-on-amz-comp" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td rowspan="1" class="rowspan-td"> Node Classification </td> <td rowspan="1" class="rowspan-td"> AMZ Photo </td> <td rowspan="1" class="rowspan-td model-col"> SIGN </td> <td style="vertical-align: top; padding-top: 18px;"> Accuracy </td> <td> 91.72 ± 1.20 </td> <td> # 13 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=15934"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-classification-on-amz-photo" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td rowspan="1" class="rowspan-td"> Node Classification </td> <td rowspan="1" class="rowspan-td"> Coauthor CS </td> <td rowspan="1" class="rowspan-td model-col"> SIGN </td> <td style="vertical-align: top; padding-top: 18px;"> Accuracy </td> <td> 91.98 ± 0.50 </td> <td> # 17 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=15935"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-classification-on-coauthor-cs" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td rowspan="4" class="rowspan-td"> Node Property Prediction </td> <td rowspan="4" class="rowspan-td"> ogbn-arxiv </td> <td rowspan="4" class="rowspan-td model-col"> SIGN </td> <td style="vertical-align: top; padding-top: 18px;"> Test Accuracy </td> <td> 0.7195 ± 0.0011 </td> <td> # 69 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=36003"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-arxiv" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Validation Accuracy </td> <td> 0.7323 ± 0.0006 </td> <td> # 65 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=36003"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-arxiv" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Number of params </td> <td> 3566128 </td> <td> # 12 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=36003"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-arxiv" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Ext. data </td> <td> No </td> <td> # 1 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=36003"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-arxiv" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td rowspan="4" class="rowspan-td"> Node Property Prediction </td> <td rowspan="4" class="rowspan-td"> ogbn-mag </td> <td rowspan="4" class="rowspan-td model-col"> SIGN </td> <td style="vertical-align: top; padding-top: 18px;"> Test Accuracy </td> <td> 0.4046 ± 0.0012 </td> <td> # 32 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=107014"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-mag" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Validation Accuracy </td> <td> 0.4068 ± 0.0010 </td> <td> # 33 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=107014"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-mag" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Number of params </td> <td> 3724645 </td> <td> # 35 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=107014"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-mag" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Ext. data </td> <td> No </td> <td> # 1 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=107014"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-mag" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td rowspan="4" class="rowspan-td"> Node Property Prediction </td> <td rowspan="4" class="rowspan-td"> ogbn-papers100M </td> <td rowspan="4" class="rowspan-td model-col"> SIGN </td> <td style="vertical-align: top; padding-top: 18px;"> Test Accuracy </td> <td> 0.6568 ± 0.0006 </td> <td> # 17 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=36019"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-papers100m" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Validation Accuracy </td> <td> 0.6932 ± 0.0006 </td> <td> # 17 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=36019"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-papers100m" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Number of params </td> <td> 1008812 </td> <td> # 16 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=36019"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-papers100m" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Ext. data </td> <td> No </td> <td> # 1 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=36019"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-papers100m" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td rowspan="4" class="rowspan-td"> Node Property Prediction </td> <td rowspan="4" class="rowspan-td"> ogbn-papers100M </td> <td rowspan="4" class="rowspan-td model-col"> SIGN-XL </td> <td style="vertical-align: top; padding-top: 18px;"> Test Accuracy </td> <td> 0.6606 ± 0.0019 </td> <td> # 15 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=36018"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-papers100m" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Validation Accuracy </td> <td> 0.6984 ± 0.0006 </td> <td> # 15 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=36018"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-papers100m" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Number of params </td> <td> 7180460 </td> <td> # 12 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=36018"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-papers100m" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Ext. data </td> <td> No </td> <td> # 1 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=36018"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-papers100m" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td rowspan="4" class="rowspan-td"> Node Property Prediction </td> <td rowspan="4" class="rowspan-td"> ogbn-products </td> <td rowspan="4" class="rowspan-td model-col"> SIGN </td> <td style="vertical-align: top; padding-top: 18px;"> Test Accuracy </td> <td> 0.8052 ± 0.0016 </td> <td> # 43 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=35932"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-products" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Validation Accuracy </td> <td> 0.9299 ± 0.0004 </td> <td> # 24 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=35932"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-products" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Number of params </td> <td> 3483703 </td> <td> # 8 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=35932"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-products" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td style="vertical-align: top; padding-top: 18px;"> Ext. data </td> <td> No </td> <td> # 1 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=35932"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-property-prediction-on-ogbn-products" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td rowspan="1" class="rowspan-td"> Node Classification </td> <td rowspan="1" class="rowspan-td"> PPI </td> <td rowspan="1" class="rowspan-td model-col"> SIGN </td> <td style="vertical-align: top; padding-top: 18px;"> F1 </td> <td> 96.50 </td> <td> # 17 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=15933"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-classification-on-ppi" style="text-decoration: underline"> Compare</a> </div> </td> </tr> <tr> <td rowspan="1" class="rowspan-td"> Node Classification </td> <td rowspan="1" class="rowspan-td"> Reddit </td> <td rowspan="1" class="rowspan-td model-col"> SIGN </td> <td style="vertical-align: top; padding-top: 18px;"> Accuracy </td> <td> 96.60% </td> <td> # 8 </td> <td class="results-icon"> <a href="/paper/sign-scalable-inception-graph-neural-networks/review/?hl=15932"> <span class=" icon-wrapper icon-ion" data-name="enter-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M176 176v-40a40 40 0 0 1 40-40h208a40 40 0 0 1 40 40v240a40 40 0 0 1-40 40H216a40 40 0 0 1-40-40v-40" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M272 336l80-80-80-80M48 256h288"/></svg></span> </a> </td> <td> <div class="sota-table-link"> <a class="btn btn-primary" href="/sota/node-classification-on-reddit" style="text-decoration: underline"> Compare</a> </div> </td> </tr> </table> </div> </div> </div> </div> <div class="row"> <div id="methods" class="col-md-12 paper-evaluation-section-title"> <div class="paper-section-title"> <div class="row"> 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