{"url":"/sota/graph-classification-on-aids","task":{"name":"Graph Classification","url":"/task/graph-classification","note":null},"dataset":{"name":"AIDS","url":"/dataset/aids"},"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","Inference Time (ms)"],"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","Inference Time (ms)":"lower"}},"counts":{"rows":2,"rows_with_code":1,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"FIT-GNN","metrics":{"Accuracy":"84.3","Inference Time (ms)":"0.00155"},"uses_additional_data":false,"paper_date":"2024-10-19","paper":"/paper/faster-inference-time-for-gnns-using","paper_url":"https://arxiv.org/abs/2410.15001v2","paper_title":"FIT-GNN: Faster Inference Time for GNNs Using Coarsening","code":"https://github.com/Roy-Shubhajit/FIT-GNN","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"DGCNN","metrics":{"Accuracy":"65.1"},"uses_additional_data":false,"paper_date":"2017-12-10","paper":"/paper/dgcnn-disordered-graph-convolutional-neural","paper_url":"http://arxiv.org/abs/1712.03563v1","paper_title":"DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model","code":null,"n_code_links":0,"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":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"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":0,"n_unverified":0,"n_samples":0,"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"}}}