{"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/on-clustering-network-valued-data","title":"On clustering network-valued data","arxiv_id":"1606.02401","date":"2016-06-08","proceeding":"NeurIPS 2017 12","authors":["Soumendu Sundar Mukherjee","Purnamrita Sarkar","Lizhen Lin"],"abstract":"Community detection, which focuses on clustering nodes or detecting\ncommunities in (mostly) a single network, is a problem of considerable\npractical interest and has received a great deal of attention in the research\ncommunity. While being able to cluster within a network is important, there are\nemerging needs to be able to cluster multiple networks. This is largely\nmotivated by the routine collection of network data that are generated from\npotentially different populations. These networks may or may not have node\ncorrespondence. When node correspondence is present, we cluster networks by\nsummarizing a network by its graphon estimate, whereas when node correspondence\nis not present, we propose a novel solution for clustering such networks by\nassociating a computationally feasible feature vector to each network based on\ntrace of powers of the adjacency matrix. We illustrate our methods using both\nsimulated and real data sets, and theoretical justifications are provided in\nterms of consistency.","url_abs":"http://arxiv.org/abs/1606.02401v3","url_pdf":"http://arxiv.org/pdf/1606.02401v3.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":"on-clustering-network-valued-data","repo_url":"https://github.com/soumendu041/clustering-network-valued-data","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":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}