{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/leveraging-node-attributes-for-incomplete","title":"Leveraging Node Attributes for Incomplete Relational Data","arxiv_id":"1706.04289","date":"2017-06-14","proceeding":"ICML 2017 8","authors":["He Zhao","Lan Du","Wray Buntine"],"abstract":"Relational data are usually highly incomplete in practice, which inspires us\nto leverage side information to improve the performance of community detection\nand link prediction. This paper presents a Bayesian probabilistic approach that\nincorporates various kinds of node attributes encoded in binary form in\nrelational models with Poisson likelihood. Our method works flexibly with both\ndirected and undirected relational networks. The inference can be done by\nefficient Gibbs sampling which leverages sparsity of both networks and node\nattributes. Extensive experiments show that our models achieve the\nstate-of-the-art link prediction results, especially with highly incomplete\nrelational data.","url_abs":"http://arxiv.org/abs/1706.04289v1","url_pdf":"http://arxiv.org/pdf/1706.04289v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"leveraging-node-attributes-for-incomplete","repo_url":"https://github.com/ethanhezhao/NARM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"community-detection","task_name":"Community Detection"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.04289","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}