{"url":"/sota/node-classification-on-wiki-1","task":{"name":"Node Classification","url":"/task/node-classification","note":null},"dataset":{"name":"wiki","url":"/dataset/wiki"},"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":["ACCURACY"],"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"}},"counts":{"rows":2,"rows_with_code":2,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"A2DUG","metrics":{"ACCURACY":"65.13±0.07"},"uses_additional_data":false,"paper_date":"2023-06-14","paper":"/paper/why-using-either-aggregated-features-or","paper_url":"https://arxiv.org/abs/2306.08274v2","paper_title":"A Simple and Scalable Graph Neural Network for Large Directed Graphs","code":"https://github.com/seijimaekawa/a2dug","n_code_links":1,"syntology":{"n_ran":3,"n_unverified":10,"n_samples":13,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"LINKX","metrics":{"ACCURACY":"59.80±0.41"},"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}}],"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":2,"rows_with_any_sample_ran":2,"distinct_papers_with_graph_line":2,"distinct_papers_with_any_sample_ran":2,"samples_over_distinct_papers":{"n_ran":10,"n_unverified":24,"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":10,"n_unverified":24,"n_samples":34,"n_pointer_only_licence":4,"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"}}}