{"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/graph-max-shift-a-hill-climbing-method-for","title":"Graph Max Shift: A Hill-Climbing Method for Graph Clustering","arxiv_id":"2411.18794","date":"2024-11-27","proceeding":null,"authors":["Ery Arias-Castro","Elizabeth Coda","Wanli Qiao"],"abstract":"We present a method for graph clustering that is analogous with gradient ascent methods previously proposed for clustering points in space. We show that, when applied to a random geometric graph with data iid from some density with Morse regularity, the method is asymptotically consistent. Here, consistency is understood with respect to a density-level clustering defined by the partition of the support of the density induced by the basins of attraction of the density modes.","url_abs":"https://arxiv.org/abs/2411.18794v1","url_pdf":"https://arxiv.org/pdf/2411.18794v1.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":"graph-max-shift-a-hill-climbing-method-for","repo_url":"https://github.com/lizzycoda/GraphMaxShift","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"graph-clustering","task_name":"Graph Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}