{"url":"/sota/graph-classification-on-upfd-gos","task":{"name":"Graph Classification","url":"/task/graph-classification","note":null},"dataset":{"name":"UPFD-GOS","url":"/dataset/upfd-gos"},"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 (%)"],"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"}},"counts":{"rows":8,"rows_with_code":8,"rows_with_paper_page":8,"rows_dated":8,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"UPFD-SAGE","metrics":{"Accuracy (%)":"97.54"},"uses_additional_data":false,"paper_date":"2021-04-25","paper":"/paper/user-preference-aware-fake-news-detection","paper_url":"https://arxiv.org/abs/2104.12259v1","paper_title":"User Preference-aware Fake News Detection","code":"https://github.com/safe-graph/GNN-FakeNews","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"HGFND","metrics":{"Accuracy (%)":"97.46±0.30"},"uses_additional_data":false,"paper_date":"2022-12-24","paper":"/paper/nothing-stands-alone-relational-fake-news-2","paper_url":"https://arxiv.org/abs/2212.12621v1","paper_title":"Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks","code":"https://github.com/ujeong1/IEEEBigdata22_HGFND","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"UPFD-GAT","metrics":{"Accuracy (%)":"96.52"},"uses_additional_data":false,"paper_date":"2021-04-25","paper":"/paper/user-preference-aware-fake-news-detection","paper_url":"https://arxiv.org/abs/2104.12259v1","paper_title":"User Preference-aware Fake News Detection","code":"https://github.com/safe-graph/GNN-FakeNews","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"UPFD-GCNFN","metrics":{"Accuracy (%)":"96.11"},"uses_additional_data":false,"paper_date":"2021-04-25","paper":"/paper/user-preference-aware-fake-news-detection","paper_url":"https://arxiv.org/abs/2104.12259v1","paper_title":"User Preference-aware Fake News Detection","code":"https://github.com/safe-graph/GNN-FakeNews","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"GCNFN","metrics":{"Accuracy (%)":"95.90"},"uses_additional_data":false,"paper_date":"2021-04-25","paper":"/paper/user-preference-aware-fake-news-detection","paper_url":"https://arxiv.org/abs/2104.12259v1","paper_title":"User Preference-aware Fake News Detection","code":"https://github.com/safe-graph/GNN-FakeNews","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":6,"model":"UPFD-GCN","metrics":{"Accuracy (%)":"95.11"},"uses_additional_data":false,"paper_date":"2021-04-25","paper":"/paper/user-preference-aware-fake-news-detection","paper_url":"https://arxiv.org/abs/2104.12259v1","paper_title":"User Preference-aware Fake News Detection","code":"https://github.com/safe-graph/GNN-FakeNews","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":7,"model":"GNNCL","metrics":{"Accuracy (%)":"93.60"},"uses_additional_data":false,"paper_date":"2021-04-25","paper":"/paper/user-preference-aware-fake-news-detection","paper_url":"https://arxiv.org/abs/2104.12259v1","paper_title":"User Preference-aware Fake News Detection","code":"https://github.com/safe-graph/GNN-FakeNews","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":8,"model":"UPFD-BiGCN","metrics":{"Accuracy (%)":"91.27"},"uses_additional_data":false,"paper_date":"2021-04-25","paper":"/paper/user-preference-aware-fake-news-detection","paper_url":"https://arxiv.org/abs/2104.12259v1","paper_title":"User Preference-aware Fake News Detection","code":"https://github.com/safe-graph/GNN-FakeNews","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}}],"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. 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