{"url":"/sota/graph-classification-on-reddit-binary","task":{"name":"Graph Classification","url":"/task/graph-classification","note":null},"dataset":{"name":"REDDIT-BINARY","url":"/dataset/reddit-binary"},"category":"Graphs","categories":["Graphs"],"category_note":null,"description":"**Graph Classification** is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Hierarchical Graph Pooling with Structure Learning](https://github.com/cszhangzhen/HGP-SL) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy","Accuracy (10-fold)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher","Accuracy (10-fold)":"higher"}},"counts":{"rows":9,"rows_with_code":9,"rows_with_paper_page":9,"rows_dated":9,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"R-GIN + PANDA","metrics":{"Accuracy":"91.36"},"uses_additional_data":false,"paper_date":"2024-06-06","paper":"/paper/panda-expanded-width-aware-message-passing","paper_url":"https://arxiv.org/abs/2406.03671v2","paper_title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","code":"https://github.com/jeongwhanchoi/panda","n_code_links":1,"syntology":{"n_ran":7,"n_unverified":2,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"Local Topological Profile (LTP)","metrics":{"Accuracy":"91.1 ± 1.0","Accuracy (10-fold)":"91.1 ± 1.0"},"uses_additional_data":false,"paper_date":"2023-05-01","paper":"/paper/strengthening-structural-baselines-for-graph","paper_url":"https://arxiv.org/abs/2305.00724v1","paper_title":"Strengthening structural baselines for graph classification using Local Topological Profile","code":"https://github.com/j-adamczyk/ltp","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"GIN + PANDA","metrics":{"Accuracy":"91.055"},"uses_additional_data":false,"paper_date":"2024-06-06","paper":"/paper/panda-expanded-width-aware-message-passing","paper_url":"https://arxiv.org/abs/2406.03671v2","paper_title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","code":"https://github.com/jeongwhanchoi/panda","n_code_links":1,"syntology":{"n_ran":7,"n_unverified":2,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"GCN + PANDA","metrics":{"Accuracy":"80.69"},"uses_additional_data":false,"paper_date":"2024-06-06","paper":"/paper/panda-expanded-width-aware-message-passing","paper_url":"https://arxiv.org/abs/2406.03671v2","paper_title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","code":"https://github.com/jeongwhanchoi/panda","n_code_links":1,"syntology":{"n_ran":7,"n_unverified":2,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"R-GCN + PANDA","metrics":{"Accuracy":"80.2"},"uses_additional_data":false,"paper_date":"2024-06-06","paper":"/paper/panda-expanded-width-aware-message-passing","paper_url":"https://arxiv.org/abs/2406.03671v2","paper_title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","code":"https://github.com/jeongwhanchoi/panda","n_code_links":1,"syntology":{"n_ran":7,"n_unverified":2,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":6,"model":"CT-Layer","metrics":{"Accuracy":"78.45"},"uses_additional_data":false,"paper_date":"2022-06-15","paper":"/paper/diffwire-inductive-graph-rewiring-via-the","paper_url":"https://arxiv.org/abs/2206.07369v3","paper_title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","code":"https://github.com/ellisalicante/GraphRewiring-Tutorial","n_code_links":2,"syntology":null},{"rank_in_archive_order":7,"model":"GAP-Layer (Rcut)","metrics":{"Accuracy":"77.63"},"uses_additional_data":false,"paper_date":"2022-06-15","paper":"/paper/diffwire-inductive-graph-rewiring-via-the","paper_url":"https://arxiv.org/abs/2206.07369v3","paper_title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","code":"https://github.com/ellisalicante/GraphRewiring-Tutorial","n_code_links":2,"syntology":null},{"rank_in_archive_order":8,"model":"DiffWire","metrics":{"Accuracy":"77.17"},"uses_additional_data":false,"paper_date":"2022-06-15","paper":"/paper/diffwire-inductive-graph-rewiring-via-the","paper_url":"https://arxiv.org/abs/2206.07369v3","paper_title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","code":"https://github.com/ellisalicante/GraphRewiring-Tutorial","n_code_links":2,"syntology":null},{"rank_in_archive_order":9,"model":"GAP-Layer (Ncut)","metrics":{"Accuracy":"76"},"uses_additional_data":false,"paper_date":"2022-06-15","paper":"/paper/diffwire-inductive-graph-rewiring-via-the","paper_url":"https://arxiv.org/abs/2206.07369v3","paper_title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","code":"https://github.com/ellisalicante/GraphRewiring-Tutorial","n_code_links":2,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":4,"rows_with_any_sample_ran":4,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":7,"n_unverified":2,"n_samples":9,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":28,"n_unverified":8,"n_samples":36,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}