{"url":"/dataset/malnet","name":"MalNet","full_name":null,"description_markdown":"MalNet is a large public graph database, representing a large-scale ontology of software function call graphs. MalNet contains over 1.2 million graphs, averaging over 17k nodes and 39k edges per graph, across a hierarchy of 47 types and 696 families.\r\n\r\nImage Source: [Expore MalNet](https://mal-net.org/explore)","description_withheld":null,"homepage":"https://mal-net.org/","introduced_date":"2020-11-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-large-scale-database-for-graph","title":"A Large-Scale Database for Graph Representation Learning","first_author":"Scott Freitas","url":null},"license":null,"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Graph Classification","url":"/task/graph-classification","datasets_with_task":"/datasets/task/graph-classification"},{"name":"Malware Detection","url":"/task/malware-detection","datasets_with_task":"/datasets/task/malware-detection"},{"name":"Malware Type Detection","url":"/task/malware-type-detection","datasets_with_task":"/datasets/task/malware-type-detection"},{"name":"Malware Family Detection","url":"/task/malware-family-detection","datasets_with_task":"/datasets/task/malware-family-detection"}],"languages":[],"variants":["MalNet","MalNet-Tiny"],"data_loaders":[],"num_papers_in_archive":17,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/graph-classification-on-malnet-tiny","task":"Graph Classification","dataset_variant":"MalNet-Tiny","rows":4,"metrics":["Accuracy","MCC"],"first_row_in_archive_order":{"model":"ESA (Edge set attention, no positional encodings)","paper":"/paper/masked-attention-is-all-you-need-for-graphs","metrics":{"Accuracy":"94.800±0.424","MCC":"0.935±0.005"},"code_links":[{"title":"davidbuterez/edge-set-attention","url":"https://github.com/davidbuterez/edge-set-attention"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/malware-detection-on-malnet","task":"Malware Detection","dataset_variant":"MalNet","rows":3,"metrics":["F1 score"],"first_row_in_archive_order":{"model":"SHERLOCK (family)","paper":"/paper/self-supervised-vision-transformers-for","metrics":{"F1 score":"0.878"},"code_links":[{"title":"sachith500/sherlock","url":"https://github.com/sachith500/sherlock"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/unlocking-the-potential-of-classic-gnns-for","title":"Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence","date":"2025-02-13","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":4,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/masked-attention-is-all-you-need-for-graphs","title":"An end-to-end attention-based approach for learning on graphs","date":"2024-02-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/exphormer-sparse-transformers-for-graphs","title":"Exphormer: Sparse Transformers for Graphs","date":"2023-03-10","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/self-supervised-vision-transformers-for","title":"Self-Supervised Vision Transformers for Malware Detection","date":"2022-08-15","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":4,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/recipe-for-a-general-powerful-scalable-graph","title":"Recipe for a General, Powerful, Scalable Graph Transformer","date":"2022-05-25","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":21,"samples_ran":3,"samples_unverified":18,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":42,"samples_ran":12,"samples_unverified":30,"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."}