{"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/spectral-clustering-for-divide-and-conquer","title":"Spectral Clustering for Divide-and-Conquer Graph Matching","arxiv_id":"1310.1297","date":"2013-10-04","proceeding":null,"authors":["Vince Lyzinski","Daniel L. Sussman","Donniell E. Fishkind","Henry Pao","Li Chen","Joshua T. Vogelstein","Youngser Park","Carey E. Priebe"],"abstract":"We present a parallelized bijective graph matching algorithm that leverages\nseeds and is designed to match very large graphs. Our algorithm combines\nspectral graph embedding with existing state-of-the-art seeded graph matching\nprocedures. We justify our approach by proving that modestly correlated, large\nstochastic block model random graphs are correctly matched utilizing very few\nseeds through our divide-and-conquer procedure. We also demonstrate the\neffectiveness of our approach in matching very large graphs in simulated and\nreal data examples, showing up to a factor of 8 improvement in runtime with\nminimal sacrifice in accuracy.","url_abs":"http://arxiv.org/abs/1310.1297v5","url_pdf":"http://arxiv.org/pdf/1310.1297v5.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":"spectral-clustering-for-divide-and-conquer","repo_url":"https://github.com/lichen11/LSGMcode","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"graph-matching","task_name":"Graph Matching"},{"task_slug":"stochastic-block-model","task_name":"Stochastic Block Model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1310.1297","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}