{"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/a-fast-algorithm-for-the-discrete-core","title":"A Fast Algorithm for the Discrete Core/Periphery Bipartitioning Problem","arxiv_id":"1102.5511","date":"2011-02-27","proceeding":null,"authors":["Sean Z. W. Lip"],"abstract":"Various methods have been proposed in the literature to determine an optimal partitioning of the set of actors in a network into core and periphery subsets. However, these methods either work only for relatively small input sizes, or do not guarantee an optimal answer. In this paper, we propose a new algorithm to solve this problem. This algorithm is efficient and exact, allowing the optimal partitioning for networks of several thousand actors to be computed in under a second. We also show that the optimal core can be characterized as a set containing the actors with the highest degrees in the original network.","url_abs":"https://arxiv.org/abs/1102.5511v1","url_pdf":"https://arxiv.org/pdf/1102.5511v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-fast-algorithm-for-the-discrete-core","repo_url":"https://github.com/skojaku/core-periphery-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}