{"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/covariate-regularized-community-detection-in","title":"Covariate Regularized Community Detection in Sparse Graphs","arxiv_id":"1607.02675","date":"2016-07-10","proceeding":null,"authors":["Bowei Yan","Purnamrita Sarkar"],"abstract":"In this paper, we investigate community detection in networks in the presence\nof node covariates. In many instances, covariates and networks individually\nonly give a partial view of the cluster structure. One needs to jointly infer\nthe full cluster structure by considering both. In statistics, an emerging body\nof work has been focused on combining information from both the edges in the\nnetwork and the node covariates to infer community memberships. However, so far\nthe theoretical guarantees have been established in the dense regime, where the\nnetwork can lead to perfect clustering under a broad parameter regime, and\nhence the role of covariates is often not clear. In this paper, we examine\nsparse networks in conjunction with finite dimensional sub-gaussian mixtures as\ncovariates under moderate separation conditions. In this setting each\nindividual source can only cluster a non-vanishing fraction of nodes correctly.\nWe propose a simple optimization framework which provably improves clustering\naccuracy when the two sources carry partial information about the cluster\nmemberships, and hence perform poorly on their own. Our optimization problem\ncan be solved using scalable convex optimization algorithms. Using a variety of\nsimulated and real data examples, we show that the proposed method outperforms\nother existing methodology.","url_abs":"http://arxiv.org/abs/1607.02675v4","url_pdf":"http://arxiv.org/pdf/1607.02675v4.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":"covariate-regularized-community-detection-in","repo_url":"https://github.com/boweiYan/SDP_SBM_unbalanced_size","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"community-detection","task_name":"Community Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1607.02675","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}