{"url":"/sota/node-classification-on-texas-60-20-20-random","task":{"name":"Node Classification","url":"/task/node-classification","note":null},"dataset":{"name":"Texas (60%/20%/20% random splits)","url":null},"category":"Graphs","categories":["Graphs"],"category_note":null,"description":"**Node Classification** is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.\r\n\r\n**Node Classification** models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like [Cora](/dataset/cora), [Citeseer](/dataset/citeseer), and [Pubmed](/dataset/pubmed), among others, typically using Accuracy and F1.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Fast Graph Representation Learning With PyTorch Geometric](https://arxiv.org/pdf/1903.02428v3.pdf) )</span>","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":36,"rows_with_code":36,"rows_with_paper_page":36,"rows_dated":36,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"ACM-GCN++","metrics":{"1:1 Accuracy":"96.56 ± 2"},"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":2,"model":"ACM-Snowball-2","metrics":{"1:1 Accuracy":"95.74 ± 2.22"},"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":3,"model":"ACMII-GCN+","metrics":{"1:1 Accuracy":"95.41 ± 2.82"},"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":4,"model":"ACMII-Snowball-2","metrics":{"1:1 Accuracy":"95.25 ± 1.55"},"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":"ACMII-GCN","metrics":{"1:1 Accuracy":"95.08 ± 2.07"},"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":"ACM-GCN+","metrics":{"1:1 Accuracy":"94.92 ± 2.79"},"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-Snowball-3","metrics":{"1:1 Accuracy":"94.75 ± 2.41"},"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":"ACMII-Snowball-3","metrics":{"1:1 Accuracy":"94.75 ± 3.09"},"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":9,"model":"ACMII-GCN++","metrics":{"1:1 Accuracy":"94.75 ±  2.91"},"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":"NFGNN","metrics":{"1:1 Accuracy":"94.03±0.82"},"uses_additional_data":false,"paper_date":"2022-12-07","paper":"/paper/node-oriented-spectral-filtering-for-graph","paper_url":"https://arxiv.org/abs/2212.03654v3","paper_title":"Node-oriented Spectral Filtering for Graph Neural Networks","code":"https://github.com/SsGood/NFGNN","n_code_links":1,"syntology":null},{"rank_in_archive_order":11,"model":"ACM-SGC-1","metrics":{"1:1 Accuracy":"93.61 ± 1.55"},"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":"ACM-SGC-2","metrics":{"1:1 Accuracy":"93.44 ± 2.54"},"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":13,"model":"ACM-GCNII*","metrics":{"1:1 Accuracy":"93.28 ± 2.79"},"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":14,"model":"BernNet","metrics":{"1:1 Accuracy":"93.12 ± 0.65"},"uses_additional_data":false,"paper_date":"2021-06-21","paper":"/paper/bernnet-learning-arbitrary-graph-spectral","paper_url":"https://arxiv.org/abs/2106.10994v3","paper_title":"BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation","code":"https://github.com/ivam-he/BernNet","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":15,"model":"GPRGNN","metrics":{"1:1 Accuracy":"92.92 ± 0.61"},"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":16,"model":"ACM-GCNII","metrics":{"1:1 Accuracy":"92.46 ± 1.97"},"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":"MLP-2","metrics":{"1:1 Accuracy":"92.26 ± 0.71"},"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":"APPNP","metrics":{"1:1 Accuracy":"91.18 ± 0.70"},"uses_additional_data":false,"paper_date":"2018-10-14","paper":"/paper/predict-then-propagate-graph-neural-networks","paper_url":"https://arxiv.org/abs/1810.05997v6","paper_title":"Predict then Propagate: Graph Neural Networks meet Personalized PageRank","code":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/appnp","n_code_links":5,"syntology":{"n_ran":1,"n_unverified":11,"n_samples":12,"n_pointer_only_licence":0}},{"rank_in_archive_order":19,"model":"FAGCN","metrics":{"1:1 Accuracy":"88.85 ± 4.39"},"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":20,"model":"GCNII*","metrics":{"1:1 Accuracy":"88.52 ± 3.02"},"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":"HH-GraphSAGE","metrics":{"1:1 Accuracy":"85.95 ± 6.42"},"uses_additional_data":false,"paper_date":"2023-08-17","paper":"/paper/half-hop-a-graph-upsampling-approach-for","paper_url":"https://arxiv.org/abs/2308.09198v1","paper_title":"Half-Hop: A graph upsampling approach for slowing down message passing","code":"https://github.com/nerdslab/halfhop","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":22,"model":"H2GCN","metrics":{"1:1 Accuracy":"85.90 ± 3.53"},"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":23,"model":"SGC-1","metrics":{"1:1 Accuracy":"83.28 ± 5.43"},"uses_additional_data":false,"paper_date":"2019-02-19","paper":"/paper/simplifying-graph-convolutional-networks","paper_url":"https://arxiv.org/abs/1902.07153v2","paper_title":"Simplifying Graph Convolutional Networks","code":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/sgc","n_code_links":7,"syntology":{"n_ran":3,"n_unverified":5,"n_samples":8,"n_pointer_only_licence":0}},{"rank_in_archive_order":24,"model":"GCN","metrics":{"1:1 Accuracy":"83.11 ± 3.2"},"uses_additional_data":false,"paper_date":"2016-09-09","paper":"/paper/semi-supervised-classification-with-graph","paper_url":"http://arxiv.org/abs/1609.02907v4","paper_title":"Semi-Supervised Classification with Graph Convolutional Networks","code":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn","n_code_links":55,"syntology":{"n_ran":31,"n_unverified":27,"n_samples":58,"n_pointer_only_licence":22}},{"rank_in_archive_order":25,"model":"Snowball-2","metrics":{"1:1 Accuracy":"83.11 ± 3.2"},"uses_additional_data":false,"paper_date":"2019-06-05","paper":"/paper/break-the-ceiling-stronger-multi-scale-deep","paper_url":"https://arxiv.org/abs/1906.02174v3","paper_title":"Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks","code":"https://github.com/PwnerHarry/Stronger_GCN","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":8,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":26,"model":"Snowball-3","metrics":{"1:1 Accuracy":"83.11 ± 3.2"},"uses_additional_data":false,"paper_date":"2019-06-05","paper":"/paper/break-the-ceiling-stronger-multi-scale-deep","paper_url":"https://arxiv.org/abs/1906.02174v3","paper_title":"Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks","code":"https://github.com/PwnerHarry/Stronger_GCN","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":8,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":27,"model":"GCNII","metrics":{"1:1 Accuracy":"82.46 ± 4.58"},"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":28,"model":"SGC-2","metrics":{"1:1 Accuracy":"81.31 ± 3.3"},"uses_additional_data":false,"paper_date":"2019-02-19","paper":"/paper/simplifying-graph-convolutional-networks","paper_url":"https://arxiv.org/abs/1902.07153v2","paper_title":"Simplifying Graph Convolutional Networks","code":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/sgc","n_code_links":7,"syntology":{"n_ran":3,"n_unverified":5,"n_samples":8,"n_pointer_only_licence":0}},{"rank_in_archive_order":29,"model":"GCN+JK","metrics":{"1:1 Accuracy":"80.66 ± 1.91"},"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":30,"model":"HH-GAT","metrics":{"1:1 Accuracy":"80.54 ± 4.80"},"uses_additional_data":false,"paper_date":"2023-08-17","paper":"/paper/half-hop-a-graph-upsampling-approach-for","paper_url":"https://arxiv.org/abs/2308.09198v1","paper_title":"Half-Hop: A graph upsampling approach for slowing down message passing","code":"https://github.com/nerdslab/halfhop","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":31,"model":"GraphSAGE","metrics":{"1:1 Accuracy":"79.03 ± 1.20"},"uses_additional_data":false,"paper_date":"2017-06-07","paper":"/paper/inductive-representation-learning-on-large","paper_url":"http://arxiv.org/abs/1706.02216v4","paper_title":"Inductive Representation Learning on Large Graphs","code":"https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/models/basic_gnn.py","n_code_links":20,"syntology":{"n_ran":3,"n_unverified":2,"n_samples":5,"n_pointer_only_licence":5}},{"rank_in_archive_order":32,"model":"GAT","metrics":{"1:1 Accuracy":"78.87 ± 0.86"},"uses_additional_data":false,"paper_date":"2017-10-30","paper":"/paper/graph-attention-networks","paper_url":"http://arxiv.org/abs/1710.10903v3","paper_title":"Graph Attention Networks","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":93,"syntology":{"n_ran":50,"n_unverified":56,"n_samples":106,"n_pointer_only_licence":43}},{"rank_in_archive_order":33,"model":"MixHop","metrics":{"1:1 Accuracy":"76.39 ± 7.66"},"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":34,"model":"GAT+JK","metrics":{"1:1 Accuracy":"75.41 ± 7.18"},"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":35,"model":"HH-GCN","metrics":{"1:1 Accuracy":"71.89 ± 3.46"},"uses_additional_data":false,"paper_date":"2023-08-17","paper":"/paper/half-hop-a-graph-upsampling-approach-for","paper_url":"https://arxiv.org/abs/2308.09198v1","paper_title":"Half-Hop: A graph upsampling approach for slowing down message passing","code":"https://github.com/nerdslab/halfhop","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":36,"model":"Geom-GCN*","metrics":{"1:1 Accuracy":"67.57"},"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}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"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":33,"rows_with_any_sample_ran":28,"distinct_papers_with_graph_line":13,"distinct_papers_with_any_sample_ran":10,"samples_over_distinct_papers":{"n_ran":116,"n_unverified":128,"n_samples":244,"n_pointer_only_licence":90,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":155,"n_unverified":220,"n_samples":375,"n_pointer_only_licence":94,"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"}}}