{"url":"/dataset/twitch-gamers","name":"twitch-gamers","full_name":null,"description_markdown":"node classification on twitch-gamers","description_withheld":null,"homepage":"","introduced_date":"2021-10-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/large-scale-learning-on-non-homophilous","title":"Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods","first_author":"Derek Lim","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"},{"name":"Node Classification on Non-Homophilic (Heterophilic) Graphs","url":"/task/node-classification-on-non-homophilic","datasets_with_task":"/datasets/task/node-classification-on-non-homophilic"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["twitch-gamers"],"data_loaders":[],"num_papers_in_archive":24,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/node-classification-on-non-homophilic-15","task":"Node Classification on Non-Homophilic (Heterophilic) Graphs","dataset_variant":"twitch-gamers","rows":26,"metrics":["1:1 Accuracy"],"first_row_in_archive_order":{"model":"GESN","paper":"/paper/addressing-heterophily-in-node-classification","metrics":{"1:1 Accuracy":"68.34 ± 0.86"},"code_links":[{"title":"dtortorella/addressing-heterophily-gesn","url":"https://github.com/dtortorella/addressing-heterophily-gesn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/node-classification-on-twitch-gamers","task":"Node Classification","dataset_variant":"twitch-gamers","rows":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Dual-Net GNN","paper":"/paper/feature-selection-key-to-enhance-node","metrics":{"Accuracy":"66.36±0.11"},"code_links":[{"title":"sunilkmaurya/DualNetGNN_large","url":"https://github.com/sunilkmaurya/DualNetGNN_large"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/addressing-heterophily-in-node-classification","title":"Addressing Heterophily in Node Classification with Graph Echo State Networks","date":"2023-05-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/feature-selection-key-to-enhance-node","title":"Feature Selection: Key to Enhance Node Classification with Graph Neural Networks","date":"2023-01-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/clenshaw-graph-neural-networks","title":"Clenshaw Graph Neural Networks","date":"2022-10-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/revisiting-heterophily-for-graph-neural","title":"Revisiting Heterophily For Graph Neural Networks","date":"2022-10-14","rows_on_this_dataset":6,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":2,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; 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not a correctness claim."}},{"paper":"/paper/simple-and-deep-graph-convolutional-networks-1","title":"Simple and Deep Graph Convolutional Networks","date":"2020-07-04","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/joint-adaptive-feature-smoothing-and-topology","title":"Adaptive Universal Generalized PageRank Graph Neural Network","date":"2020-06-14","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mixhop-higher-order-graph-convolution","title":"MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing","date":"2019-04-30","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/simplifying-graph-convolutional-networks","title":"Simplifying Graph Convolutional Networks","date":"2019-02-19","rows_on_this_dataset":2,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":3,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/predict-then-propagate-graph-neural-networks","title":"Predict then Propagate: Graph Neural Networks meet Personalized PageRank","date":"2018-10-14","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":1,"samples_unverified":11,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/semi-supervised-classification-with-graph","title":"Semi-Supervised Classification with Graph Convolutional Networks","date":"2016-09-09","rows_on_this_dataset":1,"code_links":55,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":58,"samples_ran":31,"samples_unverified":27,"pointer_only_for_licence":22,"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":8,"samples_harvested":120,"samples_ran":51,"samples_unverified":69,"pointer_only_for_licence":34,"papers_with_no_sample_that_ran":1,"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."}