{"url":"/sota/node-classification-on-wikipedia","task":{"name":"Node Classification","url":"/task/node-classification","note":null},"dataset":{"name":"Wikipedia","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":["Macro-F1","Accuracy"],"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","Accuracy":"higher"}},"counts":{"rows":6,"rows_with_code":6,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"GraphGAN","metrics":{"Accuracy":"21.3%","Macro-F1":"0.194"},"uses_additional_data":false,"paper_date":"2017-11-22","paper":"/paper/graphgan-graph-representation-learning-with","paper_url":"http://arxiv.org/abs/1711.08267v1","paper_title":"GraphGAN: Graph Representation Learning with Generative Adversarial Nets","code":"https://github.com/hwwang55/GraphGAN","n_code_links":5,"syntology":null},{"rank_in_archive_order":2,"model":"Struc2vec","metrics":{"Accuracy":"21.10%","Macro-F1":"0.190"},"uses_additional_data":false,"paper_date":"2017-04-11","paper":"/paper/struc2vec-learning-node-representations-from","paper_url":"http://arxiv.org/abs/1704.03165v3","paper_title":"struc2vec: Learning Node Representations from Structural Identity","code":"https://github.com/shenweichen/GraphEmbedding","n_code_links":5,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"DeepWalk","metrics":{"Accuracy":"19.4%","Macro-F1":"0.183"},"uses_additional_data":false,"paper_date":"2014-03-26","paper":"/paper/deepwalk-online-learning-of-social","paper_url":"http://arxiv.org/abs/1403.6652v2","paper_title":"DeepWalk: Online Learning of Social Representations","code":"https://github.com/PaddlePaddle/PaddleRec/tree/master/models/recall/deepwalk","n_code_links":14,"syntology":{"n_ran":2,"n_unverified":1,"n_samples":3,"n_pointer_only_licence":1}},{"rank_in_archive_order":4,"model":"LINE","metrics":{"Accuracy":"17.50%","Macro-F1":"0.164"},"uses_additional_data":false,"paper_date":"2015-03-12","paper":"/paper/line-large-scale-information-network","paper_url":"http://arxiv.org/abs/1503.03578v1","paper_title":"LINE: Large-scale Information Network Embedding","code":"https://github.com/shenweichen/GraphEmbedding","n_code_links":9,"syntology":{"n_ran":0,"n_unverified":3,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"node2vec","metrics":{"Accuracy":"19.1%","Macro-F1":"0.1552"},"uses_additional_data":false,"paper_date":"2016-07-03","paper":"/paper/node2vec-scalable-feature-learning-for","paper_url":"http://arxiv.org/abs/1607.00653v1","paper_title":"node2vec: Scalable Feature Learning for Networks","code":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/node2vec","n_code_links":20,"syntology":{"n_ran":8,"n_unverified":17,"n_samples":25,"n_pointer_only_licence":3}},{"rank_in_archive_order":6,"model":"DeepWalk","metrics":{"Macro-F1":"0.1274"},"uses_additional_data":false,"paper_date":"2016-07-03","paper":"/paper/node2vec-scalable-feature-learning-for","paper_url":"http://arxiv.org/abs/1607.00653v1","paper_title":"node2vec: Scalable Feature Learning for Networks","code":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/node2vec","n_code_links":20,"syntology":{"n_ran":8,"n_unverified":17,"n_samples":25,"n_pointer_only_licence":3}}],"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":5,"rows_with_any_sample_ran":4,"distinct_papers_with_graph_line":4,"distinct_papers_with_any_sample_ran":3,"samples_over_distinct_papers":{"n_ran":13,"n_unverified":21,"n_samples":34,"n_pointer_only_licence":4,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":21,"n_unverified":38,"n_samples":59,"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"}}}