{"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/efficiently-inferring-community-structure-in","title":"Efficiently inferring community structure in bipartite networks","arxiv_id":"1403.2933","date":"2014-03-12","proceeding":null,"authors":["Daniel B. Larremore","Aaron Clauset","Abigail Z. Jacobs"],"abstract":"Bipartite networks are a common type of network data in which there are two\ntypes of vertices, and only vertices of different types can be connected. While\nbipartite networks exhibit community structure like their unipartite\ncounterparts, existing approaches to bipartite community detection have\ndrawbacks, including implicit parameter choices, loss of information through\none-mode projections, and lack of interpretability. Here we solve the community\ndetection problem for bipartite networks by formulating a bipartite stochastic\nblock model, which explicitly includes vertex type information and may be\ntrivially extended to $k$-partite networks. This bipartite stochastic block\nmodel yields a projection-free and statistically principled method for\ncommunity detection that makes clear assumptions and parameter choices and\nyields interpretable results. We demonstrate this model's ability to\nefficiently and accurately find community structure in synthetic bipartite\nnetworks with known structure and in real-world bipartite networks with unknown\nstructure, and we characterize its performance in practical contexts.","url_abs":"http://arxiv.org/abs/1403.2933v2","url_pdf":"http://arxiv.org/pdf/1403.2933v2.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":"efficiently-inferring-community-structure-in","repo_url":"https://github.com/sayali-sonawane/LinkPrediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"community-detection","task_name":"Community Detection"},{"task_slug":"stochastic-block-model","task_name":"Stochastic Block Model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}