{"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/efficient-discovery-of-overlapping","title":"Efficient discovery of overlapping communities in massive networks","arxiv_id":null,"date":"2013-09-03","proceeding":"PNAS 2013 2013 9","authors":["Prem K. Gopalan","David M. Blei"],"abstract":"Detecting overlapping communities is essential to analyzing and\r\nexploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do\r\nnot scale to the size of networks that we regularly observe in the real\r\nworld. In this paper, we develop a scalable approach to community\r\ndetection that discovers overlapping communities in massive realworld networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and\r\na corresponding algorithm that naturally interleaves subsampling\r\nfrom the network and updating an estimate of its communities. We\r\ndemonstrate how we can discover the hidden community structure of\r\nseveral real-world networks, including 3.7 million US patents, 575,000\r\nphysics articles from the arXiv preprint server, and 875,000 connected\r\nWeb pages from the Internet. Furthermore, we demonstrate on large\r\nsimulated networks that our algorithm accurately discovers the true\r\ncommunity structure. This paper opens the door to using sophisticated statistical models to analyze massive networks.","url_abs":"https://www.pnas.org/content/110/36/14534","url_pdf":"https://www.pnas.org/content/pnas/110/36/14534.full.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":"efficient-discovery-of-overlapping","repo_url":"https://github.com/premgopalan/svinet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"community-detection","task_name":"Community Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}