OGB-LSC Dataset | Papers With Code

<!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 = 't62xksuxentU2uOtocFIxRpnC2554MjOaEhV1bK9oJkNqNMRlSb9TZi4TumOzDlE'; 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/814.49dcf06c.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_dataset.b56df998.css"><link rel="preload" as="script" href="https://production-assets.paperswithcode.com/perf/view_dataset.22fdaf24.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_dataset.b56df998.css">  <title>OGB-LSC Dataset | Papers With Code</title> <meta name="description" content="OGB Large-Scale Challenge (OGB-LSC) is a collection of three real-world datasets for advancing the state-of-the-art in large-scale graph ML. OGB-LSC provides graph datasets that are orders of magnitude larger than existing ones and covers three core graph learning tasks -- link prediction, graph regression, and node classification. OGB-LSC consists of three datasets: MAG240M-LSC, WikiKG90M-LSC, and PCQM4M-LSC. Each dataset offers an independent task. MAG240M-LSC is a heterogeneous academic graph, and the task is to predict the subject areas of papers situated in the heterogeneous graph (node classification). WikiKG90M-LSC is a knowledge graph, and the task is to impute missing triplets (link prediction). PCQM4M-LSC is a quantum chemistry dataset, and the task is to predict an important molecular property, the HOMO-LUMO gap, of a given molecule (graph regression)." />  <meta property="og:title" content="Papers with Code - OGB-LSC Dataset"> <meta property="og:description" content="OGB Large-Scale Challenge (OGB-LSC) is a collection of three real-world datasets for advancing the state-of-the-art in large-scale graph ML. OGB-LSC provides graph datasets that are orders of magnitude larger than existing ones and covers three core graph learning tasks -- link prediction, graph regression, and node classification. OGB-LSC consists of three datasets: MAG240M-LSC, WikiKG90M-LSC, and PCQM4M-LSC. Each dataset offers an independent task. MAG240M-LSC is a heterogeneous academic graph, and the task is to predict the subject areas of papers situated in the heterogeneous graph (node classification). WikiKG90M-LSC is a knowledge graph, and the task is to impute missing triplets (link prediction). PCQM4M-LSC is a quantum chemistry dataset, and the task is to predict an important molecular property, the HOMO-LUMO gap, of a given molecule (graph regression)."> <meta property="og:image" content="https://production-media.paperswithcode.com/datasets/ogb-lsc-task-overview.png"> <meta property="og:url" content="https://paperswithcode.com/dataset/ogb-lsc">  <meta name="twitter:card" content="summary_large_image"> <meta name="twitter:site" content="@paperswithcode"> <meta name="twitter:title" content="Papers with Code - OGB-LSC Dataset"> <meta name="twitter:description" content="OGB Large-Scale Challenge (OGB-LSC) is a collection of three real-world datasets for advancing the state-of-the-art in large-scale graph ML. OGB-LSC provides graph datasets that are orders of magnitude larger than existing ones and covers three core graph learning tasks -- link prediction, graph regression, and node classification. OGB-LSC consists of three datasets: MAG240M-LSC, WikiKG90M-LSC, and PCQM4M-LSC. Each dataset offers an independent task. MAG240M-LSC is a heterogeneous academic graph, and the task is to predict the subject areas of papers situated in the heterogeneous graph (node classification). WikiKG90M-LSC is a knowledge graph, and the task is to impute missing triplets (link prediction). PCQM4M-LSC is a quantum chemistry dataset, and the task is to predict an important molecular property, the HOMO-LUMO gap, of a given molecule (graph regression)."> <meta name="twitter:creator" content="@paperswithcode"> <meta name="twitter:url" content="https://paperswithcode.com/dataset/ogb-lsc"> <meta name="twitter:domain" content="paperswithcode.com">  <script type="application/ld+json">{ "@context": "http://schema.org", "@graph": { "@type": "Dataset", "@id": "ogb-lsc", "name": "OGB-LSC Dataset", "alternateName": "OGB Large-Scale Challenge", "description": "OGB Large-Scale Challenge (OGB-LSC) is a collection of three real-world datasets for advancing the state-of-the-art in large-scale graph ML. OGB-LSC provides graph datasets that are orders of magnitude larger than existing ones and covers three core graph learning tasks -- link prediction, graph regression, and node classification. \n\nOGB-LSC consists of three datasets: MAG240M-LSC, WikiKG90M-LSC, and PCQM4M-LSC. Each dataset offers an independent task.\n\n\nMAG240M-LSC is a heterogeneous academic graph, and the task is to predict the subject areas of papers situated in the heterogeneous graph (node classification).\nWikiKG90M-LSC is a knowledge graph, and the task is to impute missing triplets (link prediction).\nPCQM4M-LSC is a quantum chemistry dataset, and the task is to predict an important molecular property, the HOMO-LUMO gap, of a given molecule (graph regression).", "url": "https://paperswithcode.com/dataset/ogb-lsc", "image": "https://production-media.paperswithcode.com/datasets/ogb-lsc-task-overview.png", "headline": "OGB-LSC Dataset", "author": [ { "@type": "Person", "@id": "#Weihua_Hu", "name": "Weihua Hu", "image": "https://paperswithcode.com/static/" }, { "@type": "Person", "@id": "#Matthias_Fey", "name": "Matthias Fey", "image": "https://paperswithcode.com/static/" }, { "@type": "Person", "@id": "#Hongyu_Ren", "name": "Hongyu Ren", "image": "https://paperswithcode.com/static/" 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1.298 49.054 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.113-72.772 6.499.975 12.996 1.624 19.819 1.624 9.42 0 18.843-1.3 27.613-3.573-48.08-9.747-84.142-51.98-84.142-102.984v-1.3c13.968 7.798 30.213 12.67 47.43 13.32-28.263-18.843-46.78-51.006-46.78-87.391 0-19.492 5.196-37.36 14.294-52.954 51.654 63.674 129.3 105.258 216.364 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.827 46.782-104.934 104.934-104.934 30.214 0 57.502 12.67 76.671 33.136 23.715-4.548 46.455-13.319 66.599-25.34-7.798 24.367-24.366 44.834-46.132 57.828 21.117-2.274 41.584-8.122 60.426-16.244-14.292 20.791-32.161 39.309-52.628 54.253z"/></svg></span> </a> </li> <li class="nav-item"> <a id="signin-link" class="nav-link" href="/accounts/login?next=/dataset/ogb-lsc">Sign In</a> </li> </ul> </div> </nav>  <div class="modal fade" id="emailModal" tabindex="-1" role="dialog" aria-labelledby="emailModalLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div 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Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset If the description or image is from a different paper, please refer to it as follows: Source: [title](url) Image Source: [title](url) " class="md-sources-autocomplete textarea form-control" required id="id_description"> OGB Large-Scale Challenge (OGB-LSC) is a collection of three real-world datasets for advancing the state-of-the-art in large-scale graph ML. OGB-LSC provides graph datasets that are orders of magnitude larger than existing ones and covers three core graph learning tasks -- link prediction, graph regression, and node classification. OGB-LSC consists of three datasets: MAG240M-LSC, WikiKG90M-LSC, and PCQM4M-LSC. Each dataset offers an independent task. * MAG240M-LSC is a heterogeneous academic graph, and the task is to predict the subject areas of papers situated in the heterogeneous graph (node classification). * WikiKG90M-LSC is a knowledge graph, and the task is to impute missing triplets (link prediction). * PCQM4M-LSC is a quantum chemistry dataset, and the task is to predict an important molecular property, the HOMO-LUMO gap, of a given molecule (graph regression).</textarea> </div> </div> <div id="div_id_url" class="form-group"> <label for="id_url" class=""> Homepage URL (optional): </label> <div class=""> <input type="text" name="url" value="https://ogb.stanford.edu/kddcup2021/" class="textinput textInput form-control" id="id_url"> </div> </div> <div id="div_id_paper" class="form-group"> <label for="id_paper" class=""> Paper where the dataset was introduced: </label> <div class=""> <select name="paper" style="width: 350px" class="modelselect2 form-control custom-select" id="id_paper" data-autocomplete-light-language="en" data-autocomplete-light-url="/sota/autocomplete/paper" data-autocomplete-light-function="select2"> <option value="">---------</option> <option value="754731" selected>OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs</option> </select><div style="display:none" class="dal-forward-conf" id="dal-forward-conf-for_id_paper"><script type="text/dal-forward-conf">[{"type": "const", "val": true, "dst": "all_portals"}]</script></div> </div> </div> <div id="div_id_introduced_date" class="form-group"> <label for="id_introduced_date" class=""> Introduction date: </label> <div class=""> <input type="text" name="introduced_date" value="2021-03-17" autocomplete="off" class="dateinput form-control" id="id_introduced_date"> </div> </div> <div id="div_id_license_name" class="form-group"> <label for="id_license_name" class=""> Dataset license: </label> <div class=""> <input type="text" name="license_name" value="Unknown" maxlength="500" class="textinput 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data-autocomplete-light-url="/task-autocomplete/" data-autocomplete-light-function="select2" multiple> <option value="1679" selected>Graph Regression</option> <option value="222" selected>Knowledge Graphs</option> <option value="31" selected>Link Prediction</option> <option value="407" selected>Node Classification</option> </select><div style="display:none" class="dal-forward-conf" id="dal-forward-conf-for_id_tasks"><script type="text/dal-forward-conf">[{"type": "const", "val": true, "dst": "disable_create_option"}]</script></div> </div> </div> <p> Some tasks are inferred based on the benchmarks list. </p> <div class="modal-footer"> <button type="submit" class="btn btn-primary" name="edit-tasks"> Save </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" id="addLoader" role="dialog" aria-labelledby="addLoaderLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="addLoaderLabel">Add a Data Loader</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"> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="t62xksuxentU2uOtocFIxRpnC2554MjOaEhV1bK9oJkNqNMRlSb9TZi4TumOzDlE"> <div id="div_id_library_url" class="form-group"> <label for="id_library_url" class=" requiredField"> Code Repository URL:<span class="asteriskField">*</span> </label> <div class=""> <input type="text" name="library_url" class="textinput textInput form-control" required id="id_library_url"> </div> </div> <div id="div_id_loader_url" class="form-group"> <label for="id_loader_url" class=""> [Optional] URL to documentation for this dataset: </label> <div class=""> <input type="url" name="loader_url" class="urlinput form-control" id="id_loader_url"> </div> </div> <div id="div_id_frameworks" class="form-group"> <label class=""> Supported frameworks: </label> <div > <div class="form-check"> <input type="checkbox" class="form-check-input" name="frameworks" value="tf" id="id_frameworks_0" > <label class="form-check-label" for="id_frameworks_0"> TensorFlow </label> </div> <div class="form-check"> <input type="checkbox" class="form-check-input" name="frameworks" value="pytorch" id="id_frameworks_1" > <label class="form-check-label" for="id_frameworks_1"> PyTorch </label> </div> <div class="form-check"> <input type="checkbox" class="form-check-input" name="frameworks" value="jax" id="id_frameworks_2" > <label class="form-check-label" for="id_frameworks_2"> JAX </label> </div> </div> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary" name="add-loader"> Submit </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" id="removeLoader" tabindex="-1" role="dialog" aria-labelledby="removeLoaderLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div 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class="form-group"> <label for="id_modalities" class=""> Add or remove modalities: </label> <div class=""> <select name="modalities" data-container-css-class="" data-allow-clear="false" style="width: 100%" class="modelselect2multiple form-control" id="id_modalities" data-autocomplete-light-language="en" data-autocomplete-light-url="/dataset-collection-autocomplete/" data-autocomplete-light-function="select2" multiple> <option value="8" selected>Graphs</option> </select><div style="display:none" class="dal-forward-conf" id="dal-forward-conf-for_id_modalities"><script type="text/dal-forward-conf">[{"type": "const", "val": "Modalities", "dst": "area_name"}]</script></div> </div> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary" name="edit-modalities"> Save </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" id="editLanguages" role="dialog" aria-labelledby="editLanguagesLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="editLanguagesLabel">Edit Dataset Languages</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"> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="t62xksuxentU2uOtocFIxRpnC2554MjOaEhV1bK9oJkNqNMRlSb9TZi4TumOzDlE"> <div id="div_id_languages" class="form-group"> <label for="id_languages" class=""> Add or remove languages: </label> <div class=""> <select name="languages" data-container-css-class="" data-allow-clear="false" style="width: 100%" class="modelselect2multiple form-control" id="id_languages" data-autocomplete-light-language="en" data-autocomplete-light-url="/dataset-collection-autocomplete/" data-autocomplete-light-function="select2" multiple> </select><div style="display:none" class="dal-forward-conf" id="dal-forward-conf-for_id_languages"><script type="text/dal-forward-conf">[{"type": "const", "val": "Languages", "dst": "area_name"}]</script></div> </div> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary" name="edit-languages"> Save </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" id="editVariants" role="dialog" aria-labelledby="editVariantsLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="editVariantsLabel">Edit Dataset Variants</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"> <form action="" method="post"> <input type="hidden" name="csrfmiddlewaretoken" value="t62xksuxentU2uOtocFIxRpnC2554MjOaEhV1bK9oJkNqNMRlSb9TZi4TumOzDlE"> <div id="div_id_variants" class="form-group"> <label for="id_variants" class=""> Add or remove variants: </label> <div class=""> <select name="variants" data-container-css-class="" data-allow-clear="false" style="width: 100%" class="modelselect2multiple form-control" id="id_variants" data-autocomplete-light-language="en" data-autocomplete-light-url="/dataset-autocomplete/" data-autocomplete-light-function="select2" multiple> <option value="6452" selected>MAG240M-LSC</option> <option value="6300" selected>OGB-LSC</option> <option value="6454" selected>PCQM4M-LSC</option> <option value="6453" selected>WikiKG90M-LSC</option> </select><div style="display:none" class="dal-forward-conf" id="dal-forward-conf-for_id_variants"><script type="text/dal-forward-conf">[{"type": "const", "val": true, "dst": "disable_create_option"}]</script></div> </div> </div> <p> The benchmarks section lists all benchmarks using a given dataset or any of its variants. We use variants to distinguish between results evaluated on slightly different versions of the same dataset. For example, ImageNet 32⨉32 and ImageNet 64⨉64 are variants of the ImageNet dataset. </p> <div class="modal-footer"> <button type="submit" class="btn btn-primary" name="edit-variants"> Save </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> <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"> <input type="hidden" name="csrfmiddlewaretoken" value="t62xksuxentU2uOtocFIxRpnC2554MjOaEhV1bK9oJkNqNMRlSb9TZi4TumOzDlE"> <div id="div_id_paper" class="form-group"> <label for="id_paper_add_row" class=" requiredField"> Paper title:<span class="asteriskField">*</span> </label> <div class=""> <select name="paper" id="id_paper_add_row" class="modelselect2 form-control" required 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_dataset" class="form-group"> <label for="id_dataset" class=" requiredField"> Dataset or its variant:<span class="asteriskField">*</span> </label> <div class=""> <select name="dataset" class="modelselect2 form-control" required id="id_dataset" data-autocomplete-light-language="en" data-autocomplete-light-url="/dataset-autocomplete/" data-autocomplete-light-function="select2"> <option value="">---------</option> <option value="6300" selected>OGB-LSC</option> </select> </div> </div> <div id="div_id_task" class="form-group"> <label for="id_task" class=" requiredField"> Task:<span class="asteriskField">*</span> </label> <div class=""> <select name="task" class="modelselect2 form-control" required 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 id="div_id_model_name" class="form-group"> <label for="id_model_name" class=" requiredField"> Model name:<span class="asteriskField">*</span> </label> <div class=""> <input type="text" name="model_name" class="textinput textInput form-control" required id="id_model_name"> </div> </div> <div id="div_id_metric" class="form-group"> <label for="id_metric" class=" requiredField"> Metric name:<span class="asteriskField">*</span> </label> <div class=""> <select name="metric" class="modelselect2 form-control" required id="id_metric" data-autocomplete-light-language="en" data-autocomplete-light-url="/metric-autocomplete/" data-autocomplete-light-function="select2"> <option value="" selected>---------</option> </select> </div> </div> <div id="sota-metric-names"> </div> <div class="form-group"> <div id="div_id_metric_higher_is_better" class="form-check"> <input type="checkbox" name="metric_higher_is_better" class="checkboxinput form-check-input" id="id_metric_higher_is_better"> <label for="id_metric_higher_is_better" class="form-check-label"> Higher is better (for the metric) </label> </div> </div> <div id="div_id_metric_value" class="form-group"> <label for="id_metric_value" class=" requiredField"> Metric value:<span class="asteriskField">*</span> </label> <div class=""> <input type="text" name="metric_value" class="textinput textInput form-control" required id="id_metric_value"> </div> </div> <div id="sota-metric-values"> </div> <div class="form-group"> <div id="div_id_uses_additional_data" class="form-check"> <input type="checkbox" name="uses_additional_data" class="checkboxinput form-check-input" id="id_uses_additional_data"> <label for="id_uses_additional_data" class="form-check-label"> Uses extra training data </label> </div> </div> <div id="div_id_evaluated_on" class="form-group"> <label for="id_evaluated_on" class=""> Data evaluated on </label> <div class=""> <input type="text" name="evaluated_on" autocomplete="off" class="dateinput form-control" id="id_evaluated_on"> </div> </div> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary" name="add-row">Submit </button> </div> </form> </div> </div> </div> <div class="container content content-buffer "> <div class="mobile-width"> <div class="dataset-header"> <a href="/datasets?mod=graphs"> <span class="badge badge-primary"> <span class=" icon-wrapper icon-fa icon-fa-solid" data-name="draw-polygon"><svg viewBox="0 0 448 514.999" xmlns="http://www.w3.org/2000/svg"><path d="M384 353.998c35.35 0 64 28.65 63.99 64 0 35.35-28.65 64-64 64-23.63 0-44.03-12.95-55.12-32H119.12c-11.08 19.05-31.49 32-55.12 32-35.35 0-64-28.65-64-64 0-23.63 12.95-44.04 32-55.13v-209.75c-19.05-11.08-32-31.49-32-55.12 0-35.35 28.65-64 64-64 23.63 0 44.04 12.95 55.12 32h209.76c11.08-19.05 31.49-32 55.12-32 35.35 0 64 28.65 64 64s-28.65 64-64 64c-.35 0-.67-.09-1.02-.1l-39.2 65.32c5.08 9.17 8.22 19.56 8.22 30.78s-3.15 21.61-8.22 30.78l39.2 65.32c.35 0 .67-.1 1.02-.1zm-288 8.88c8.074 4.689 18.432 15.046 23.12 23.12h208.36l-38.46-64.1c-.35 0-.67.1-1.02.1-35.35 0-64-28.65-64-64s28.65-64 64-64c.35 0 .67.09 1.02.1l38.46-64.1H119.12c-4.69 8.073-15.048 18.43-23.12 23.12v209.76zm176-104.88c0 8.82 7.18 16 16 16s16-7.18 16-16-7.18-16-16-16-16 7.18-16 16zm128-160c0-8.82-7.18-16-16-16s-16 7.18-16 16 7.18 16 16 16 16-7.18 16-16zm-336-16c-8.82 0-16 7.18-16 16s7.18 16 16 16 16-7.18 16-16-7.18-16-16-16zm-16 336c0 8.82 7.18 16 16 16s16-7.18 16-16-7.18-16-16-16-16 7.18-16 16zm336 16c8.82 0 16-7.18 16-16s-7.18-16-16-16-16 7.18-16 16 7.18 16 16 16z"/></svg></span> <span>Graphs</span> </span> </a> </div> <div class="dataset-title mb-3"> <div class="row"> <div class="col-md-12"> <div class="float-right dataset-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 > OGB-LSC (OGB Large-Scale Challenge) </h1> <span class="dataset-subtitle">Introduced by Hu et al. in <a href="/paper/ogb-lsc-a-large-scale-challenge-for-machine">OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs</a></span> </div> </div> </div> <div class="d-md-none mb-3"> <a href="https://production-media.paperswithcode.com/datasets/ogb-lsc-task-overview.png" data-lightbox="imageresource-mobile"> <img id="imageresource-mobile" class="d-md-none" src="https://production-media.paperswithcode.com/datasets/ogb-lsc-task-overview.png"> </a> </div> <div class="dataset-content"> <div class="row"> <div class="col-md-9 description"> <div class="description-content"> <p>OGB Large-Scale Challenge (OGB-LSC) is a collection of three real-world datasets for advancing the state-of-the-art in large-scale graph ML. OGB-LSC provides graph datasets that are orders of magnitude larger than existing ones and covers three core graph learning tasks -- link prediction, graph regression, and node classification. </p> <p>OGB-LSC consists of three datasets: MAG240M-LSC, WikiKG90M-LSC, and PCQM4M-LSC. Each dataset offers an independent task.</p> <ul> <li>MAG240M-LSC is a heterogeneous academic graph, and the task is to predict the subject areas of papers situated in the heterogeneous graph (node classification).</li> <li>WikiKG90M-LSC is a knowledge graph, and the task is to impute missing triplets (link prediction).</li> <li>PCQM4M-LSC is a quantum chemistry dataset, and the task is to predict an important molecular property, the HOMO-LUMO gap, of a given molecule (graph regression).</li> </ul> </div> <div class="context"> <div class="row"> <div class="col-md-12"> <a href="https://ogb.stanford.edu/kddcup2021/" onclick="captureOutboundLink('https://ogb.stanford.edu/kddcup2021/'); return true;" class="btn btn-primary-outline dataset-homepage"> Homepage </a> </div> </div> </div> <div id="benchmarks" class="collapsed"> <h4>Benchmarks <div class="float-right"> <div class="dropdown edit-button"> <button class="dropdown-toggle badge badge-edit" type="button" id="evalEditMenu" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> <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 </button> <div class="dropdown-menu dropdown-menu-end" aria-labelledby="evalEditMenu"> <a class="dropdown-item" href="#loginModal" data-bs-toggle="modal"> <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> Add a new result</a> <a class="dropdown-item" href="#loginModal" data-bs-toggle="modal"> <span class=" icon-wrapper icon-ion" data-name="create-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M384 224v184a40 40 0 0 1-40 40H104a40 40 0 0 1-40-40V168a40 40 0 0 1 40-40h167.48" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path d="M459.94 53.25a16.06 16.06 0 0 0-23.22-.56L424.35 65a8 8 0 0 0 0 11.31l11.34 11.32a8 8 0 0 0 11.34 0l12.06-12c6.1-6.09 6.67-16.01.85-22.38zM399.34 90L218.82 270.2a9 9 0 0 0-2.31 3.93L208.16 299a3.91 3.91 0 0 0 4.86 4.86l24.85-8.35a9 9 0 0 0 3.93-2.31L422 112.66a9 9 0 0 0 0-12.66l-9.95-10a9 9 0 0 0-12.71 0z"/></svg></span> Link an existing benchmark</a> </div> </div> </div> </h4> <hr> <div class="sota-table-preview"> <table id="benchmarks-table" class="table-striped"> <thead> <tr> <th>Trend</th> <th>Task</th> <th>Dataset Variant</th> <th>Best Model</th> <th>Paper</th> <th>Code</th> </tr> </thead> <tbody> <tr onclick="window.location='/sota/graph-regression-on-pcqm4m-lsc';"> <td> <a href="/sota/graph-regression-on-pcqm4m-lsc"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/graph-regression-on-pcqm4m-lsc-small_05a4ffac.png"/> </a> </td> <td> <div class="task black-links"> <a style="font-weight: 600;" href="/sota/graph-regression-on-pcqm4m-lsc"> Graph Regression </a> </div> </td> <td> <div style="font-weight: 500;"> PCQM4M-LSC </div> </td> <td> <div class="method-name" style="font-weight: 500;"> Graphormer </div> </td> <td> <div class="text-center paper"> <a href="/paper/do-transformers-really-perform-bad-for-graph"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/do-transformers-really-perform-bad-for-graph#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> </tr> <tr onclick="window.location='/sota/node-classification-on-mag240m-lsc';"> <td> <a href="/sota/node-classification-on-mag240m-lsc"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/node-classification-on-mag240m-lsc-small_7e5ac954.png"/> </a> </td> <td> <div class="task black-links"> <a style="font-weight: 600;" href="/sota/node-classification-on-mag240m-lsc"> Node Classification </a> </div> </td> <td> <div style="font-weight: 500;"> MAG240M-LSC </div> </td> <td> <div class="method-name" style="font-weight: 500;"> R-GraphSAGE </div> </td> <td> <div class="text-center paper"> <a href="/paper/ogb-lsc-a-large-scale-challenge-for-machine"> <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/ogb-lsc-a-large-scale-challenge-for-machine#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> </tr> <tr onclick="window.location='/sota/knowledge-graphs-on-wikikg90m-lsc';"> <td> <a href="/sota/knowledge-graphs-on-wikikg90m-lsc"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/knowledge-graphs-on-wikikg90m-lsc-small_3befde90.png"/> </a> </td> <td> <div class="task black-links"> <a style="font-weight: 600;" href="/sota/knowledge-graphs-on-wikikg90m-lsc"> Knowledge Graphs </a> </div> </td> <td> <div style="font-weight: 500;"> WikiKG90M-LSC </div> </td> <td> <div class="method-name" style="font-weight: 500;"> TransE-Concat </div> </td> <td> <div class="text-center paper"> <a href="/paper/ogb-lsc-a-large-scale-challenge-for-machine"> <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/ogb-lsc-a-large-scale-challenge-for-machine#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> </tr> </tbody> </table> </div> </div> <div id="papers" class="dataset-papers"> <h4 style="margin-bottom: 0.5rem">Papers</h4> <hr> <div class="sota-table-preview papers-datatable-component"> <table style="width: 100% !important;" id="datatable-papers" class="table-striped table-responsive"> <thead style="width: 100% !important;"> <tr> <th style="text-left"><span>Paper</span></th> <th class="text-center"><span>Code</span></th> <th class="text-center"><span>Results</span></th> <th class="text-right"><span>Date</span></th> <th class="text-center"><span>Stars</span></th> </tr> </thead> </table> </div> <script> const DATATABLE_PAPERS_FILTER_NAME = 'paperdataset__dataset_id'; const DATATABLE_PAPERS_FILTER_VALUE = '6300'; </script> </div> <div class="dataloader-container"> <div> <h4> Dataset Loaders <div class="float-right"> <div class="dropdown edit-button" style="position: relative; top: -5px"> <button class="dropdown-toggle badge badge-edit" type="button" id="loaderEditMenu" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> <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 </button> <div class="dropdown-menu dropdown-menu-end" aria-labelledby="loaderEditMenu"> <a class="dropdown-item" href="#loginModal" data-bs-toggle="modal"> <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> Add</a> <a class="dropdown-item" href="#loginModal" data-bs-toggle="modal"> <span class=" icon-wrapper icon-ion" data-name="remove"><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="M400 256H112"/></svg></span> Remove</a> </div> </div> </h4> <hr> </div> <div class="row"> <div class="col-md-12"> <div class="dataloader-empty"> No data loaders found. You can <a href="#loginModal" data-bs-toggle="modal"> submit your data loader here. </a> </div> </div> </div> </div> <div class="collections"> <h4> Tasks <div class="float-right"> <div class="dropdown edit-button" style="position: relative; top: -5px"> <button class="badge badge-edit" type="button" data-bs-target="#loginModal" data-bs-toggle="modal" aria-haspopup="true" aria-expanded="false"> <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 </button> </div> </h4> <hr> </div> <div class="row"> <div class="col-md-12"> <ul class="list-unstyled"> <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> </li> <li> <a href="/task/link-prediction"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000031-326cd034.jpg"> <span>Link Prediction</span> </span> </a> </li> <li> <a href="/task/knowledge-graphs"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Knowledge Graphs</span> </span> </a> </li> <div class="collapse" id="remaining-tasks"> <li> <a href="/task/graph-regression"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Graph Regression</span></span> </a> </li> </div> <button class="dropdown-toggle badge badge-edit w-100 collapsed" type="button" data-bs-target="#remaining-tasks" data-bs-toggle="collapse" aria-haspopup="true" aria-expanded="false" onclick="this.innerHTML=this.classList.contains('collapsed')?'Show less':'Show all'" > Show all </button> </ul> </div> </div> <div class="dataset-similar"> <h4> Similar Datasets </h4> <hr> </div> <div class="row"> <div class="col dataset-similar"> <div class="row"> <div class="col-md-12 featured-dataset"> <div class="card-deck card-break" style="margin-bottom:20px;"> <div class="card" style="min-height:120px"> <a href="/dataset/hpo"> <div class="row"> <div class="col-lg-12"> <div class="item-image" style="background-image: url(https://production-media.paperswithcode.com/datasets/250c0162-9367-41d5-9352-4f8b39c0c330.png);"> </div> </div> </div> <div class=row"> <h1>HPO</h1> </div> </a> </div> <div class="card" style="min-height:120px"> <a href="/dataset/pcqm4mv2-lsc"> <div class="row"> <div class="col-lg-12"> <div class="item-image" style="background-image: url(https://production-media.paperswithcode.com/datasets/95804466-0394-4d25-8e07-5eeaaec0f45a.png);"> </div> </div> </div> <div class=row"> <h1>PCQM4Mv2-LSC</h1> </div> </a> </div> </div> </div> </div> </div> </div> </div> <div class="col-md-3 dataset-infobox"> <div class="mb-3 d-none d-md-block"> <a href="https://production-media.paperswithcode.com/datasets/ogb-lsc-task-overview.png" data-lightbox="imageresource"> <img id="imageresource" width=100% src="https://production-media.paperswithcode.com/datasets/ogb-lsc-task-overview.png"> </a> </div> <div id="trends"> <h4>Usage <span data-bs-toggle="tooltip" data-bs-placement="top" data-bs-trigger="hover" title="Approximate number of open-access papers mentioning the dataset in the last five years." style="font-size: 16px; vertical-align: text-bottom; color: #444;" > <span class=" icon-wrapper icon-ion" data-name="flask-outline"><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-miterlimit="10" stroke-width="32" d="M176 48h160M118 304h276M208 48v93.48a64.09 64.09 0 0 1-9.88 34.18L73.21 373.49C48.4 412.78 76.63 464 123.08 464h265.84c46.45 0 74.68-51.22 49.87-90.51L313.87 175.66a64.09 64.09 0 0 1-9.87-34.18V48"/></svg></span> </span> </h4> <hr> <figure class="highcharts-figure"> <div id="container"></div> </figure> </div> <div class="license"> <h4>License<a href="/datasets/license" target="_blank" class="datasets-license-info"><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></a> <div class="float-right"> <div class="dropdown edit-button" style="position: relative; top: -5px"> <button class="badge badge-edit" type="button" data-bs-target="#loginModal" data-bs-toggle="modal" aria-haspopup="true" aria-expanded="false"> <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 </button> </div> </h4> <hr> <ul class="list-unstyled"> <li> <span class="license-static-badge"> Unknown </span> </li> </ul> </div> <div class="collections"> <h4> Modalities <div class="float-right"> <div class="dropdown edit-button" style="position: relative; top: -5px"> <button class="badge badge-edit" type="button" data-bs-target="#loginModal" data-bs-toggle="modal" aria-haspopup="true" aria-expanded="false"> <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 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