{"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/parallel-graph-partitioning-for-complex","title":"Parallel Graph Partitioning for Complex Networks","arxiv_id":"1404.4797","date":"2014-04-18","proceeding":null,"authors":["Henning Meyerhenke","Peter Sanders","Christian Schulz"],"abstract":"Processing large complex networks like social networks or web graphs has\nrecently attracted considerable interest. In order to do this in parallel, we\nneed to partition them into pieces of about equal size. Unfortunately, previous\nparallel graph partitioners originally developed for more regular mesh-like\nnetworks do not work well for these networks. This paper addresses this problem\nby parallelizing and adapting the label propagation technique originally\ndeveloped for graph clustering. By introducing size constraints, label\npropagation becomes applicable for both the coarsening and the refinement phase\nof multilevel graph partitioning. We obtain very high quality by applying a\nhighly parallel evolutionary algorithm to the coarsened graph. The resulting\nsystem is both more scalable and achieves higher quality than state-of-the-art\nsystems like ParMetis or PT-Scotch. For large complex networks the performance\ndifferences are very big. For example, our algorithm can partition a web graph\nwith 3.3 billion edges in less than sixteen seconds using 512 cores of a high\nperformance cluster while producing a high quality partition -- none of the\ncompeting systems can handle this graph on our system.","url_abs":"http://arxiv.org/abs/1404.4797v3","url_pdf":"http://arxiv.org/pdf/1404.4797v3.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":"parallel-graph-partitioning-for-complex","repo_url":"https://github.com/KaHIP/KaHIP","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"graph-clustering","task_name":"Graph Clustering"},{"task_slug":"graph-partitioning","task_name":"graph partitioning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}