{"url":"/sota/link-prediction-on-wiki","task":{"name":"Link Prediction","url":"/task/link-prediction","note":null},"dataset":{"name":"Wiki","url":"/dataset/wiki"},"category":"Natural Language Processing","categories":["Graphs","Natural Language Processing"],"category_note":null,"description":"**Link Prediction** is a task in graph and network analysis where the goal is to predict missing or future connections between nodes in a network. Given a partially observed network, the goal of link prediction is to infer which links are most likely to be added or missing based on the observed connections and the structure of the network.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Inductive Representation Learning on Large Graphs](https://arxiv.org/pdf/1706.02216v4.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":["AUC"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"AUC":"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":"","metrics":{"AUC":"200%"},"uses_additional_data":false,"paper_date":"2022-03-01","paper":"/paper/an-effective-graph-learning-based-approach","paper_url":"https://arxiv.org/abs/2203.01820v1","paper_title":"An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022","code":"https://github.com/im0qianqian/WSDM2022TGP-AntGraph","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"BANE","metrics":{"AUC":"90.90%"},"uses_additional_data":false,"paper_date":"2018-10-22","paper":"/paper/binarized-attributed-network-embedding","paper_url":"https://www.researchgate.net/publication/328688614_Binarized_Attributed_Network_Embedding","paper_title":"Binarized Attributed Network Embedding","code":"https://github.com/benedekrozemberczki/karateclub","n_code_links":2,"syntology":null}],"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":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"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"}}}