{"url":"/dataset/city-networks","name":"City-Networks","full_name":null,"description_markdown":"City-Networks, a transductive learning dataset for testing long-range dependencies in Graph Neural Networks (GNNs). In particular, the dataset contains four large-scale city maps: Paris, Shanghai, L.A., and London, where nodes represent intersections and edges represent road segments.","description_withheld":null,"homepage":"https://github.com/LeonResearch/City-Networks","introduced_date":"2025-03-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/towards-quantifying-long-range-interactions","title":"Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement","first_author":"Huidong Liang","url":null},"license":null,"modalities":[],"tasks":[{"name":"Node Classification","url":"/task/node-classification","datasets_with_task":"/datasets/task/node-classification"}],"languages":[],"variants":["City-Networks","Paris","Shanghai","Log Angeles","London"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/node-classification-on-log-angeles","task":"Node Classification","dataset_variant":"Log Angeles","rows":5,"metrics":["Average Top-1 Accuracy"],"first_row_in_archive_order":{"model":"IM-GCN","paper":"/paper/improving-the-effective-receptive-field-of","metrics":{"Average Top-1 Accuracy":"62.4 ± 0.3"},"code_links":[{"title":"bgu-cs-vil/im-mpnn","url":"https://github.com/bgu-cs-vil/im-mpnn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-london","task":"Node Classification","dataset_variant":"London","rows":5,"metrics":["Average Top-1 Accuracy"],"first_row_in_archive_order":{"model":"IM-GCN","paper":"/paper/improving-the-effective-receptive-field-of","metrics":{"Average Top-1 Accuracy":"58.9 ± 0.1"},"code_links":[{"title":"bgu-cs-vil/im-mpnn","url":"https://github.com/bgu-cs-vil/im-mpnn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-paris","task":"Node Classification","dataset_variant":"Paris","rows":5,"metrics":["Average Top-1 Accuracy"],"first_row_in_archive_order":{"model":"IM-GCN","paper":"/paper/improving-the-effective-receptive-field-of","metrics":{"Average Top-1 Accuracy":"55.3  ± 0.3"},"code_links":[{"title":"bgu-cs-vil/im-mpnn","url":"https://github.com/bgu-cs-vil/im-mpnn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-shanghai","task":"Node Classification","dataset_variant":"Shanghai","rows":5,"metrics":["Average Top-1 Accuracy"],"first_row_in_archive_order":{"model":"IM-GCN","paper":"/paper/improving-the-effective-receptive-field-of","metrics":{"Average Top-1 Accuracy":"67.8 ± 0.1"},"code_links":[{"title":"bgu-cs-vil/im-mpnn","url":"https://github.com/bgu-cs-vil/im-mpnn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/improving-the-effective-receptive-field-of","title":"Improving the Effective Receptive Field of Message-Passing Neural Networks","date":"2025-05-29","rows_on_this_dataset":4,"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/towards-quantifying-long-range-interactions","title":"Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement","date":"2025-03-12","rows_on_this_dataset":16,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"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":2,"samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":3,"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."}