{"url":"/dataset/aids","name":"AIDS","full_name":"AIDS","description_markdown":"**AIDS** is a graph dataset. It consists of 2000 graphs representing molecular compounds which are constructed from the AIDS Antiviral Screen Database of Active Compounds. It contains 4395 chemical compounds, of which 423 belong to class CA, 1081 to CM, and the remaining compounds to CI.\r\n\r\nSource: [DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model](https://arxiv.org/abs/1712.03563)\r\nImage Source: [https://www.researchgate.net/figure/Sample-component-in-AIDS-kernel-dataset-with-Graphwave-based-structural-role-colors-Here_fig1_338282222](https://www.researchgate.net/figure/Sample-component-in-AIDS-kernel-dataset-with-Graphwave-based-structural-role-colors-Here_fig1_338282222)","description_withheld":null,"homepage":"http://networkrepository.com/AIDS.php","introduced_date":"2008-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning","first_author":null,"url":"https://doi.org/10.1007/978-3-540-89689-0_33"},"license":{"name":"CC BY-SA","url":"http://networkrepository.com/policy.php"},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Graph Classification","url":"/task/graph-classification","datasets_with_task":"/datasets/task/graph-classification"},{"name":"Continual Learning","url":"/task/continual-learning","datasets_with_task":"/datasets/task/continual-learning"}],"languages":[],"variants":["AIDS"],"data_loaders":[],"num_papers_in_archive":67,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/graph-classification-on-aids","task":"Graph Classification","dataset_variant":"AIDS","rows":2,"metrics":["Accuracy","Inference Time (ms)"],"first_row_in_archive_order":{"model":"FIT-GNN","paper":"/paper/faster-inference-time-for-gnns-using","metrics":{"Accuracy":"84.3","Inference Time (ms)":"0.00155"},"code_links":[{"title":"Roy-Shubhajit/FIT-GNN","url":"https://github.com/Roy-Shubhajit/FIT-GNN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/continual-learning-on-aids","task":"Continual Learning","dataset_variant":"AIDS","rows":1,"metrics":["1:3 Accuracy"],"first_row_in_archive_order":{"model":"TEST","paper":"/paper/advances-and-challenges-in-foundation-agents","metrics":{"1:3 Accuracy":"2"},"code_links":[{"title":"foundationagents/awesome-foundation-agents","url":"https://github.com/foundationagents/awesome-foundation-agents"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/advances-and-challenges-in-foundation-agents","title":"Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems","date":"2025-03-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/faster-inference-time-for-gnns-using","title":"FIT-GNN: Faster Inference Time for GNNs Using Coarsening","date":"2024-10-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dgcnn-disordered-graph-convolutional-neural","title":"DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model","date":"2017-12-10","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}