{"url":"/sota/graph-classification-on-reddit-b","task":{"name":"Graph Classification","url":"/task/graph-classification","note":null},"dataset":{"name":"REDDIT-B","url":"/dataset/reddit"},"category":"Graphs","categories":["Graphs"],"category_note":null,"description":"**Graph Classification** is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Hierarchical Graph Pooling with Structure Learning](https://github.com/cszhangzhen/HGP-SL) )</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":["Accuracy","Accuracy (10-fold)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher","Accuracy (10-fold)":"higher"}},"counts":{"rows":12,"rows_with_code":12,"rows_with_paper_page":12,"rows_dated":12,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"CRaWl","metrics":{"Accuracy":"93.15"},"uses_additional_data":false,"paper_date":"2021-02-17","paper":"/paper/graph-learning-with-1d-convolutions-on-random","paper_url":"https://arxiv.org/abs/2102.08786v3","paper_title":"Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing","code":"https://github.com/toenshoff/CRaWl","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"GAT-GC (f-Scaled)","metrics":{"Accuracy":"92.57"},"uses_additional_data":false,"paper_date":"2019-07-04","paper":"/paper/improving-attention-mechanism-in-graph-neural","paper_url":"https://arxiv.org/abs/1907.02204v4","paper_title":"Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation","code":"https://github.com/zetayue/CPA","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":"GIN-0","metrics":{"Accuracy":"92.4"},"uses_additional_data":false,"paper_date":"2018-10-01","paper":"/paper/how-powerful-are-graph-neural-networks","paper_url":"http://arxiv.org/abs/1810.00826v3","paper_title":"How Powerful are Graph Neural Networks?","code":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/gin","n_code_links":19,"syntology":{"n_ran":3,"n_unverified":7,"n_samples":10,"n_pointer_only_licence":5}},{"rank_in_archive_order":4,"model":"DiffPool","metrics":{"Accuracy":"92.1"},"uses_additional_data":false,"paper_date":"2019-03-06","paper":"/paper/fast-graph-representation-learning-with","paper_url":"http://arxiv.org/abs/1903.02428v3","paper_title":"Fast Graph Representation Learning with PyTorch Geometric","code":"https://github.com/rusty1s/pytorch_geometric","n_code_links":6,"syntology":{"n_ran":0,"n_unverified":2,"n_samples":2,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"WEGL","metrics":{"Accuracy":"92"},"uses_additional_data":false,"paper_date":"2020-06-16","paper":"/paper/wasserstein-embedding-for-graph-learning","paper_url":"https://arxiv.org/abs/2006.09430v2","paper_title":"Wasserstein Embedding for Graph Learning","code":"https://github.com/navid-naderi/WEGL","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"Local Topological Profile (LTP)","metrics":{"Accuracy":"91.1 ± 1.0","Accuracy (10-fold)":"91.1 ± 1.0"},"uses_additional_data":false,"paper_date":"2023-05-01","paper":"/paper/strengthening-structural-baselines-for-graph","paper_url":"https://arxiv.org/abs/2305.00724v1","paper_title":"Strengthening structural baselines for graph classification using Local Topological Profile","code":"https://github.com/j-adamczyk/ltp","n_code_links":1,"syntology":null},{"rank_in_archive_order":7,"model":"2-WL-GNN","metrics":{"Accuracy":"89.4"},"uses_additional_data":false,"paper_date":"2020-07-01","paper":"/paper/a-novel-higher-order-weisfeiler-lehman-graph","paper_url":"https://arxiv.org/abs/2007.00346v2","paper_title":"A Novel Higher-order Weisfeiler-Lehman Graph Convolution","code":"https://github.com/Cortys/master-thesis","n_code_links":1,"syntology":null},{"rank_in_archive_order":8,"model":"δ-2-LWL","metrics":{"Accuracy":"89.0"},"uses_additional_data":false,"paper_date":"2019-04-02","paper":"/paper/towards-a-practical-k-dimensional-weisfeiler","paper_url":"https://arxiv.org/abs/1904.01543v3","paper_title":"Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings","code":"https://github.com/chrsmrrs/sparsewl","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":9,"model":"NDP","metrics":{"Accuracy":"84.3"},"uses_additional_data":false,"paper_date":"2019-10-24","paper":"/paper/hierarchical-representation-learning-in-graph","paper_url":"https://arxiv.org/abs/1910.11436v3","paper_title":"Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling","code":"https://github.com/danielegrattarola/decimation-pooling","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":10,"model":"GraphSAGE","metrics":{"Accuracy":"84.3"},"uses_additional_data":false,"paper_date":"2019-12-20","paper":"/paper/a-fair-comparison-of-graph-neural-networks-1","paper_url":"https://arxiv.org/abs/1912.09893v3","paper_title":"A Fair Comparison of Graph Neural Networks for Graph Classification","code":"https://github.com/diningphil/gnn-comparison","n_code_links":5,"syntology":null},{"rank_in_archive_order":11,"model":"ApproxRepSet","metrics":{"Accuracy":"80.3"},"uses_additional_data":false,"paper_date":"2019-04-03","paper":"/paper/rep-the-set-neural-networks-for-learning-set","paper_url":"https://arxiv.org/abs/1904.01962v2","paper_title":"Rep the Set: Neural Networks for Learning Set Representations","code":"https://github.com/giannisnik/repset","n_code_links":1,"syntology":null},{"rank_in_archive_order":12,"model":"Graph-JEPA","metrics":{"Accuracy":"56.73"},"uses_additional_data":false,"paper_date":"2023-09-27","paper":"/paper/graph-level-representation-learning-with","paper_url":"https://arxiv.org/abs/2309.16014v3","paper_title":"Graph-level Representation Learning with Joint-Embedding Predictive Architectures","code":"https://github.com/geriskenderi/graph-jepa","n_code_links":1,"syntology":{"n_ran":10,"n_unverified":1,"n_samples":11,"n_pointer_only_licence":0}}],"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":6,"rows_with_any_sample_ran":3,"distinct_papers_with_graph_line":6,"distinct_papers_with_any_sample_ran":3,"samples_over_distinct_papers":{"n_ran":14,"n_unverified":12,"n_samples":26,"n_pointer_only_licence":7,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":14,"n_unverified":12,"n_samples":26,"n_pointer_only_licence":7,"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"}}}