{"url":"/sota/graph-classification-on-frankenstein","task":{"name":"Graph Classification","url":"/task/graph-classification","note":null},"dataset":{"name":"FRANKENSTEIN","url":null},"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":6,"rows_with_code":5,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"GWL_WL","metrics":{"Accuracy":"78.9"},"uses_additional_data":false,"paper_date":"2015-07-25","paper":"/paper/graph-invariant-kernels","paper_url":"https://dl.acm.org/citation.cfm?id=2832747.2832773","paper_title":"Graph Invariant Kernels","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"MEWISPool","metrics":{"Accuracy":"73.46"},"uses_additional_data":false,"paper_date":"2021-07-03","paper":"/paper/maximum-entropy-weighted-independent-set","paper_url":"https://arxiv.org/abs/2107.01410v1","paper_title":"Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks","code":"https://github.com/mewispool/mewispool","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":3,"model":"MAGPool","metrics":{"Accuracy":"67.21"},"uses_additional_data":false,"paper_date":"2024-03-04","paper":"/paper/multi-hop-attention-based-graph-pooling-a","paper_url":"https://ieeexplore.ieee.org/document/10454077","paper_title":"Multi-hop Attention-based Graph Pooling: A Personalized PageRank Perspective","code":"https://github.com/p-haddadian/MAGPool","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"ASAP","metrics":{"Accuracy":"66.26"},"uses_additional_data":false,"paper_date":"2019-11-18","paper":"/paper/asap-adaptive-structure-aware-pooling-for","paper_url":"https://arxiv.org/abs/1911.07979v3","paper_title":"ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations","code":"https://github.com/malllabiisc/ASAP","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"SAGPool_g","metrics":{"Accuracy":"62.57"},"uses_additional_data":false,"paper_date":"2019-04-17","paper":"/paper/self-attention-graph-pooling","paper_url":"https://arxiv.org/abs/1904.08082v4","paper_title":"Self-Attention Graph Pooling","code":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/sagpool","n_code_links":3,"syntology":null},{"rank_in_archive_order":6,"model":"SAGPool_h","metrics":{"Accuracy":"61.73"},"uses_additional_data":false,"paper_date":"2019-04-17","paper":"/paper/self-attention-graph-pooling","paper_url":"https://arxiv.org/abs/1904.08082v4","paper_title":"Self-Attention Graph Pooling","code":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/sagpool","n_code_links":3,"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":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":2,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":2,"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"}}}