{"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/community-detection-over-a-heterogeneous","title":"Community detection over a heterogeneous population of non-aligned networks","arxiv_id":"1904.05332","date":"2019-04-04","proceeding":null,"authors":["Guilherme Gomes","Vinayak Rao","Jennifer Neville"],"abstract":"Clustering and community detection with multiple graphs have typically\nfocused on aligned graphs, where there is a mapping between nodes across the\ngraphs (e.g., multi-view, multi-layer, temporal graphs). However, there are\nnumerous application areas with multiple graphs that are only partially\naligned, or even unaligned. These graphs are often drawn from the same\npopulation, with communities of potentially different sizes that exhibit\nsimilar structure. In this paper, we develop a joint stochastic blockmodel\n(Joint SBM) to estimate shared communities across sets of heterogeneous\nnon-aligned graphs. We derive an efficient spectral clustering approach to\nlearn the parameters of the joint SBM. We evaluate the model on both synthetic\nand real-world datasets and show that the joint model is able to exploit\ncross-graph information to better estimate the communities compared to learning\nseparate SBMs on each individual graph.","url_abs":"http://arxiv.org/abs/1904.05332v1","url_pdf":"http://arxiv.org/pdf/1904.05332v1.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":"community-detection-over-a-heterogeneous","repo_url":"https://github.com/kurtmaia/JointSBM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"community-detection","task_name":"Community Detection"}],"methods":[{"method_slug":"spectral-clustering","method_name":"Spectral Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}