{"url":"/sota/heterogeneous-node-classification-on-acm","task":{"name":"Heterogeneous Node Classification","url":"/task/heterogeneous-node-classification","note":null},"dataset":{"name":"ACM (Heterogeneous Node Classification)","url":"/dataset/acm-heterogeneous-node-classification"},"category":"Graphs","categories":["Graphs"],"category_note":null,"description":"Node classification in heterogeneous graphs, where nodes and/or edges have multiple types.","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":[" Macro-F1","Micro-F1","Claim Classification Macro-F1","Macro-F1"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{" Macro-F1":"higher","Micro-F1":"higher","Claim Classification Macro-F1":"higher","Macro-F1":"higher"}},"counts":{"rows":11,"rows_with_code":11,"rows_with_paper_page":11,"rows_dated":11,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"RpHGNN","metrics":{" Macro-F1":"94.09","Micro-F1":"94.04"},"uses_additional_data":false,"paper_date":"2023-10-23","paper":"/paper/efficient-heterogeneous-graph-learning-via","paper_url":"https://arxiv.org/abs/2310.14481v2","paper_title":"Efficient Heterogeneous Graph Learning via Random Projection","code":"https://github.com/CrawlScript/RpHGNN","n_code_links":1,"syntology":{"n_ran":5,"n_unverified":3,"n_samples":8,"n_pointer_only_licence":8}},{"rank_in_archive_order":2,"model":"SlotGAT","metrics":{" Macro-F1":"93.99","Micro-F1":"94.06"},"uses_additional_data":false,"paper_date":"2024-05-03","paper":"/paper/slotgat-slot-based-message-passing-for","paper_url":"https://arxiv.org/abs/2405.01927v1","paper_title":"SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network","code":"https://github.com/scottjiao/slotgat_icml23","n_code_links":1,"syntology":{"n_ran":6,"n_unverified":1,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"Simple-HGN","metrics":{" Macro-F1":"93.42","Micro-F1":"93.35"},"uses_additional_data":false,"paper_date":"2021-12-30","paper":"/paper/are-we-really-making-much-progress-revisiting","paper_url":"https://arxiv.org/abs/2112.14936v1","paper_title":"Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks","code":"https://github.com/thudm/hgb","n_code_links":2,"syntology":null},{"rank_in_archive_order":4,"model":"NARS","metrics":{" Macro-F1":"93.36","Micro-F1":"93.31"},"uses_additional_data":false,"paper_date":"2020-11-19","paper":"/paper/scalable-graph-neural-networks-for-1","paper_url":"https://arxiv.org/abs/2011.09679v1","paper_title":"Scalable Graph Neural Networks for Heterogeneous Graphs","code":"https://github.com/facebookresearch/NARS","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"GCN","metrics":{" Macro-F1":"92.17","Micro-F1":"92.12"},"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":6,"model":"RGCN","metrics":{" Macro-F1":"91.55","Micro-F1":"91.41"},"uses_additional_data":false,"paper_date":"2017-03-17","paper":"/paper/modeling-relational-data-with-graph","paper_url":"http://arxiv.org/abs/1703.06103v4","paper_title":"Modeling Relational Data with Graph Convolutional Networks","code":"https://github.com/dmlc/dgl/tree/master/examples/tensorflow/rgcn","n_code_links":27,"syntology":{"n_ran":10,"n_unverified":22,"n_samples":32,"n_pointer_only_licence":15}},{"rank_in_archive_order":7,"model":"GTN","metrics":{" Macro-F1":"91.31","Micro-F1":"91.20"},"uses_additional_data":false,"paper_date":"2019-11-06","paper":"/paper/graph-transformer-networks-1","paper_url":"https://arxiv.org/abs/1911.06455v2","paper_title":"Graph Transformer Networks","code":"https://github.com/seongjunyun/Graph_Transformer_Networks","n_code_links":1,"syntology":null},{"rank_in_archive_order":8,"model":"HGT","metrics":{" Macro-F1":"91.12","Micro-F1":"91.00"},"uses_additional_data":false,"paper_date":"2020-03-03","paper":"/paper/heterogeneous-graph-transformer","paper_url":"https://arxiv.org/abs/2003.01332v1","paper_title":"Heterogeneous Graph Transformer","code":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/hgt","n_code_links":4,"syntology":{"n_ran":1,"n_unverified":5,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":9,"model":"HetSANN","metrics":{" Macro-F1":"90.02","Micro-F1":"89.91"},"uses_additional_data":false,"paper_date":"2019-12-19","paper":"/paper/an-attention-based-graph-neural-network-for","paper_url":"https://arxiv.org/abs/1912.10832v1","paper_title":"An Attention-based Graph Neural Network for Heterogeneous Structural Learning","code":"https://github.com/didi/hetsann","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":3,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":10,"model":"SeHGNN","metrics":{"Claim Classification Macro-F1":"93.95","Micro-F1":"93.87"},"uses_additional_data":false,"paper_date":"2022-07-06","paper":"/paper/simple-and-efficient-heterogeneous-graph","paper_url":"https://arxiv.org/abs/2207.02547v3","paper_title":"Simple and Efficient Heterogeneous Graph Neural Network","code":"https://github.com/ict-gimlab/sehgnn","n_code_links":2,"syntology":null},{"rank_in_archive_order":11,"model":"GAT","metrics":{"Macro-F1":"92.26","Micro-F1":"92.19"},"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}}],"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":7,"rows_with_any_sample_ran":6,"distinct_papers_with_graph_line":7,"distinct_papers_with_any_sample_ran":6,"samples_over_distinct_papers":{"n_ran":103,"n_unverified":117,"n_samples":220,"n_pointer_only_licence":88,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":103,"n_unverified":117,"n_samples":220,"n_pointer_only_licence":88,"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"}}}