{"url":"/sota/node-classification-on-non-homophilic-8","task":{"name":"Node Classification on Non-Homophilic (Heterophilic) Graphs","url":"/task/node-classification-on-non-homophilic","note":null},"dataset":{"name":"Wisconsin (48%/32%/20% fixed splits)","url":"/dataset/wisconsin-48-32-20-fixed-splits"},"category":"Graphs","categories":["Graphs"],"category_note":null,"description":"There exists a non-trivial set of graphs where graph-aware models underperform their corresponding graph-agnostic models, e.g. SGC and GCN underperform MLP with 1 layer and 2 layers. Although still controversial, people believe the performance degradation results from heterophily, i.e. there exist much more inter-class edges than inner-class edges. This task aims to evaluate models designed for non-homophilic (heterophilic) datasets.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["1:1 Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"1:1 Accuracy":"higher"}},"counts":{"rows":26,"rows_with_code":26,"rows_with_paper_page":26,"rows_dated":26,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"O(d)-NSD","metrics":{"1:1 Accuracy":"89.41 ± 4.74"},"uses_additional_data":false,"paper_date":"2022-02-09","paper":"/paper/neural-sheaf-diffusion-a-topological","paper_url":"https://arxiv.org/abs/2202.04579v4","paper_title":"Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs","code":"https://github.com/twitter-research/neural-sheaf-diffusion","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"Gen-NSD","metrics":{"1:1 Accuracy":"89.21 ± 3.84"},"uses_additional_data":false,"paper_date":"2022-02-09","paper":"/paper/neural-sheaf-diffusion-a-topological","paper_url":"https://arxiv.org/abs/2202.04579v4","paper_title":"Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs","code":"https://github.com/twitter-research/neural-sheaf-diffusion","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"Diag-NSD","metrics":{"1:1 Accuracy":"88.63 ± 2.75"},"uses_additional_data":false,"paper_date":"2022-02-09","paper":"/paper/neural-sheaf-diffusion-a-topological","paper_url":"https://arxiv.org/abs/2202.04579v4","paper_title":"Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs","code":"https://github.com/twitter-research/neural-sheaf-diffusion","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"ACM-GCN","metrics":{"1:1 Accuracy":"88.43 ± 3.22"},"uses_additional_data":false,"paper_date":"2022-10-14","paper":"/paper/revisiting-heterophily-for-graph-neural","paper_url":"https://arxiv.org/abs/2210.07606v1","paper_title":"Revisiting Heterophily For Graph Neural Networks","code":"https://github.com/SitaoLuan/ACM-GNN","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":5,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"ACM-GCN+","metrics":{"1:1 Accuracy":"88.43 ± 2.39"},"uses_additional_data":false,"paper_date":"2022-10-14","paper":"/paper/revisiting-heterophily-for-graph-neural","paper_url":"https://arxiv.org/abs/2210.07606v1","paper_title":"Revisiting Heterophily For Graph Neural Networks","code":"https://github.com/SitaoLuan/ACM-GNN","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":5,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":6,"model":"ACMII-GCN++","metrics":{"1:1 Accuracy":"88.43 ± 3.66"},"uses_additional_data":false,"paper_date":"2022-10-14","paper":"/paper/revisiting-heterophily-for-graph-neural","paper_url":"https://arxiv.org/abs/2210.07606v1","paper_title":"Revisiting Heterophily For Graph Neural Networks","code":"https://github.com/SitaoLuan/ACM-GNN","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":5,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":7,"model":"ACM-GCN++","metrics":{"1:1 Accuracy":"88.24 ± 3.16"},"uses_additional_data":false,"paper_date":"2022-10-14","paper":"/paper/revisiting-heterophily-for-graph-neural","paper_url":"https://arxiv.org/abs/2210.07606v1","paper_title":"Revisiting Heterophily For Graph Neural Networks","code":"https://github.com/SitaoLuan/ACM-GNN","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":5,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":8,"model":"GloGNN++","metrics":{"1:1 Accuracy":" 88.04 ± 3.22 "},"uses_additional_data":false,"paper_date":"2022-05-15","paper":"/paper/finding-global-homophily-in-graph-neural","paper_url":"https://arxiv.org/abs/2205.07308v1","paper_title":"Finding Global Homophily in Graph Neural Networks When Meeting Heterophily","code":"https://github.com/recklessronan/glognn","n_code_links":1,"syntology":null},{"rank_in_archive_order":9,"model":"ACMII-GCN+","metrics":{"1:1 Accuracy":"88.04 ± 3.66"},"uses_additional_data":false,"paper_date":"2022-10-14","paper":"/paper/revisiting-heterophily-for-graph-neural","paper_url":"https://arxiv.org/abs/2210.07606v1","paper_title":"Revisiting Heterophily For Graph Neural Networks","code":"https://github.com/SitaoLuan/ACM-GNN","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":5,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":10,"model":"H2GCN","metrics":{"1:1 Accuracy":"87.65 ± 4.98"},"uses_additional_data":false,"paper_date":"2020-06-20","paper":"/paper/generalizing-graph-neural-networks-beyond","paper_url":"https://arxiv.org/abs/2006.11468v2","paper_title":"Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs","code":"https://github.com/GemsLab/H2GCN","n_code_links":4,"syntology":{"n_ran":13,"n_unverified":8,"n_samples":21,"n_pointer_only_licence":6}},{"rank_in_archive_order":11,"model":"ACMII-GCN","metrics":{"1:1 Accuracy":"87.45 ± 3.74"},"uses_additional_data":false,"paper_date":"2022-10-14","paper":"/paper/revisiting-heterophily-for-graph-neural","paper_url":"https://arxiv.org/abs/2210.07606v1","paper_title":"Revisiting Heterophily For Graph Neural Networks","code":"https://github.com/SitaoLuan/ACM-GNN","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":5,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":12,"model":"NLMLP","metrics":{"1:1 Accuracy":"87.3 ± 4.3 "},"uses_additional_data":false,"paper_date":"2020-05-29","paper":"/paper/non-local-graph-neural-networks","paper_url":"https://arxiv.org/abs/2005.14612v2","paper_title":"Non-Local Graph Neural Networks","code":"https://github.com/divelab/Non-Local-GNN","n_code_links":1,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":13,"model":"GloGNN","metrics":{"1:1 Accuracy":"87.06 ± 3.53 "},"uses_additional_data":false,"paper_date":"2022-05-15","paper":"/paper/finding-global-homophily-in-graph-neural","paper_url":"https://arxiv.org/abs/2205.07308v1","paper_title":"Finding Global Homophily in Graph Neural Networks When Meeting Heterophily","code":"https://github.com/recklessronan/glognn","n_code_links":1,"syntology":null},{"rank_in_archive_order":14,"model":"WRGAT","metrics":{"1:1 Accuracy":"86.98 ± 3.78 "},"uses_additional_data":false,"paper_date":"2021-06-11","paper":"/paper/breaking-the-limit-of-graph-neural-networks","paper_url":"https://arxiv.org/abs/2106.06586v1","paper_title":"Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns","code":"https://github.com/susheels/gnns-and-local-assortativity","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":11,"n_samples":12,"n_pointer_only_licence":0}},{"rank_in_archive_order":15,"model":"GGCN","metrics":{"1:1 Accuracy":"86.86 ± 3.29 "},"uses_additional_data":false,"paper_date":"2021-02-12","paper":"/paper/two-sides-of-the-same-coin-heterophily-and","paper_url":"https://arxiv.org/abs/2102.06462v8","paper_title":"Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks","code":"https://github.com/yujun-yan/heterophily_and_oversmoothing","n_code_links":1,"syntology":null},{"rank_in_archive_order":16,"model":"ACM-SGC-1","metrics":{"1:1 Accuracy":"86.47 ± 3.77"},"uses_additional_data":false,"paper_date":"2022-10-14","paper":"/paper/revisiting-heterophily-for-graph-neural","paper_url":"https://arxiv.org/abs/2210.07606v1","paper_title":"Revisiting Heterophily For Graph Neural Networks","code":"https://github.com/SitaoLuan/ACM-GNN","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":5,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":17,"model":"ACM-SGC-2","metrics":{"1:1 Accuracy":"86.47 ± 3.77"},"uses_additional_data":false,"paper_date":"2022-10-14","paper":"/paper/revisiting-heterophily-for-graph-neural","paper_url":"https://arxiv.org/abs/2210.07606v1","paper_title":"Revisiting Heterophily For Graph Neural Networks","code":"https://github.com/SitaoLuan/ACM-GNN","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":5,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":18,"model":"GESN","metrics":{"1:1 Accuracy":"83.33 ± 3.81"},"uses_additional_data":false,"paper_date":"2023-05-14","paper":"/paper/addressing-heterophily-in-node-classification","paper_url":"https://arxiv.org/abs/2305.08233v2","paper_title":"Addressing Heterophily in Node Classification with Graph Echo State Networks","code":"https://github.com/dtortorella/addressing-heterophily-gesn","n_code_links":1,"syntology":null},{"rank_in_archive_order":19,"model":"GPRGCN","metrics":{"1:1 Accuracy":"82.55 ± 6.23"},"uses_additional_data":false,"paper_date":"2020-06-14","paper":"/paper/joint-adaptive-feature-smoothing-and-topology","paper_url":"https://arxiv.org/abs/2006.07988v6","paper_title":"Adaptive Universal Generalized PageRank Graph Neural Network","code":"https://github.com/jianhao2016/GPRGNN","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":2,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":20,"model":"GCNII","metrics":{"1:1 Accuracy":"80.39 ± 3.40"},"uses_additional_data":false,"paper_date":"2020-07-04","paper":"/paper/simple-and-deep-graph-convolutional-networks-1","paper_url":"https://arxiv.org/abs/2007.02133v1","paper_title":"Simple and Deep Graph Convolutional Networks","code":"https://github.com/chennnM/GCNII/tree/master/PyG/ogbn-arxiv","n_code_links":4,"syntology":{"n_ran":5,"n_unverified":2,"n_samples":7,"n_pointer_only_licence":4}},{"rank_in_archive_order":21,"model":"FAGCN","metrics":{"1:1 Accuracy":"79.61 ± 1.58"},"uses_additional_data":false,"paper_date":"2021-01-04","paper":"/paper/beyond-low-frequency-information-in-graph","paper_url":"https://arxiv.org/abs/2101.00797v1","paper_title":"Beyond Low-frequency Information in Graph Convolutional Networks","code":"https://github.com/bdy9527/FAGCN","n_code_links":1,"syntology":null},{"rank_in_archive_order":22,"model":"MixHop","metrics":{"1:1 Accuracy":"75.88 ± 4.90 "},"uses_additional_data":false,"paper_date":"2019-04-30","paper":"/paper/mixhop-higher-order-graph-convolution","paper_url":"https://arxiv.org/abs/1905.00067v3","paper_title":"MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing","code":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/mixhop","n_code_links":3,"syntology":null},{"rank_in_archive_order":23,"model":"LINKX","metrics":{"1:1 Accuracy":"75.49 ± 5.72"},"uses_additional_data":false,"paper_date":"2021-10-27","paper":"/paper/large-scale-learning-on-non-homophilous","paper_url":"https://arxiv.org/abs/2110.14446v1","paper_title":"Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods","code":"https://github.com/cuai/non-homophily-large-scale","n_code_links":5,"syntology":{"n_ran":7,"n_unverified":14,"n_samples":21,"n_pointer_only_licence":4}},{"rank_in_archive_order":24,"model":"Geom-GCN","metrics":{"1:1 Accuracy":"64.51 ± 3.66"},"uses_additional_data":false,"paper_date":"2020-02-13","paper":"/paper/geom-gcn-geometric-graph-convolutional-1","paper_url":"https://arxiv.org/abs/2002.05287v2","paper_title":"Geom-GCN: Geometric Graph Convolutional Networks","code":"https://github.com/bingzhewei/geom-gcn","n_code_links":4,"syntology":{"n_ran":7,"n_unverified":0,"n_samples":7,"n_pointer_only_licence":7}},{"rank_in_archive_order":25,"model":"NLGCN","metrics":{"1:1 Accuracy":"60.2 ± 5.3 "},"uses_additional_data":false,"paper_date":"2020-05-29","paper":"/paper/non-local-graph-neural-networks","paper_url":"https://arxiv.org/abs/2005.14612v2","paper_title":"Non-Local Graph Neural Networks","code":"https://github.com/divelab/Non-Local-GNN","n_code_links":1,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":26,"model":"NLGAT","metrics":{"1:1 Accuracy":"56.9 ± 7.3"},"uses_additional_data":false,"paper_date":"2020-05-29","paper":"/paper/non-local-graph-neural-networks","paper_url":"https://arxiv.org/abs/2005.14612v2","paper_title":"Non-Local Graph Neural Networks","code":"https://github.com/divelab/Non-Local-GNN","n_code_links":1,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":3}}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":20,"rows_with_any_sample_ran":16,"distinct_papers_with_graph_line":9,"distinct_papers_with_any_sample_ran":7,"samples_over_distinct_papers":{"n_ran":38,"n_unverified":43,"n_samples":81,"n_pointer_only_licence":26,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":58,"n_unverified":80,"n_samples":138,"n_pointer_only_licence":32,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}