{"url":"/dataset/usa-air-traffic","name":"USA Air-Traffic","full_name":"USA Air-Traffic","description_markdown":"Leonardo Filipe Rodrigues Ribeiro, Pedro H. P. Saverese, and Daniel R. Figueiredo. struc2vec: Learning node\r\nrepresentations from structural identity.","description_withheld":null,"homepage":"","introduced_date":"2017-04-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/struc2vec-learning-node-representations-from","title":"struc2vec: Learning Node Representations from Structural Identity","first_author":"Leonardo F. R. Ribeiro","url":null},"license":null,"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Node Classification","url":"/task/node-classification","datasets_with_task":"/datasets/task/node-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["USA Air-Traffic"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/node-classification-on-usa-air-traffic","task":"Node Classification","dataset_variant":"USA Air-Traffic","rows":7,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UGT","paper":"/paper/transitivity-preserving-graph-representation","metrics":{"Accuracy":"66.22±4.55"},"code_links":[{"title":"nslab-cuk/unified-graph-transformer","url":"https://github.com/nslab-cuk/unified-graph-transformer"},{"title":"nslab-cuk/community-aware-graph-transformer","url":"https://github.com/nslab-cuk/community-aware-graph-transformer"},{"title":"nslab-cuk/literalkg","url":"https://github.com/nslab-cuk/literalkg"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/transitivity-preserving-graph-representation","title":"Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity","date":"2023-08-18","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":26,"samples_ran":10,"samples_unverified":16,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/demo-net-degree-specific-graph-neural","title":"DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification","date":"2019-06-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deeper-insights-into-graph-convolutional","title":"Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning","date":"2018-01-22","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/graph-attention-networks","title":"Graph Attention Networks","date":"2017-10-30","rows_on_this_dataset":1,"code_links":93,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":106,"samples_ran":51,"samples_unverified":55,"pointer_only_for_licence":43,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/inductive-representation-learning-on-large","title":"Inductive Representation Learning on Large Graphs","date":"2017-06-07","rows_on_this_dataset":1,"code_links":20,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/revisiting-semi-supervised-learning-with","title":"Revisiting Semi-Supervised Learning with Graph Embeddings","date":"2016-03-29","rows_on_this_dataset":1,"code_links":26,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":28,"samples_ran":15,"samples_unverified":13,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":4,"samples_harvested":165,"samples_ran":79,"samples_unverified":86,"pointer_only_for_licence":52,"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."}