{"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/190412593","title":"Density-based Community Detection/Optimization","arxiv_id":"1904.12593","date":"2019-04-07","proceeding":null,"authors":["Rui Portocarrero Sarmento"],"abstract":"Modularity-based algorithms used for community detection have been increasing\nin recent years. Modularity and its application have been generating\ncontroversy since some authors argue it is not a metric without disadvantages.\nIt has been shown that algorithms that use modularity to detect communities\nsuffer a resolution limit and, therefore, it is unable to identify small\ncommunities in some situations. In this work, we try to apply a density\noptimization of communities found by the label propagation algorithm and study\nwhat happens regarding modularity of optimized results. We introduce a metric\nwe call ADC (Average Density per Community); we use this metric to prove our\noptimization provides improvements to the community density obtained with\nbenchmark algorithms. Additionally, we provide evidence this optimization might\nnot alter modularity of resulting communities significantly. Additionally, by\nalso using the SSC (Strongly Connected Components) concept we developed a\ncommunity detection algorithm that we also compare with the label propagation\nalgorithm. These comparisons were executed with several test networks and with\ndifferent network sizes. The results of the optimization algorithm proved to be\ninteresting. Additionally, the results of the community detection algorithm\nturned out to be similar to the benchmark algorithm we used.","url_abs":"http://arxiv.org/abs/1904.12593v1","url_pdf":"http://arxiv.org/pdf/1904.12593v1.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":"190412593","repo_url":"https://github.com/cran/DynComm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"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}