{"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/efficient-monte-carlo-and-greedy-heuristic","title":"Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models","arxiv_id":"1310.4378","date":"2013-10-16","proceeding":null,"authors":["Tiago P. Peixoto"],"abstract":"We present an efficient algorithm for the inference of stochastic block\nmodels in large networks. The algorithm can be used as an optimized Markov\nchain Monte Carlo (MCMC) method, with a fast mixing time and a much reduced\nsusceptibility to getting trapped in metastable states, or as a greedy\nagglomerative heuristic, with an almost linear $O(N\\ln^2N)$ complexity, where\n$N$ is the number of nodes in the network, independent on the number of blocks\nbeing inferred. We show that the heuristic is capable of delivering results\nwhich are indistinguishable from the more exact and numerically expensive MCMC\nmethod in many artificial and empirical networks, despite being much faster.\nThe method is entirely unbiased towards any specific mixing pattern, and in\nparticular it does not favor assortative community structures.","url_abs":"http://arxiv.org/abs/1310.4378v3","url_pdf":"http://arxiv.org/pdf/1310.4378v3.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":"efficient-monte-carlo-and-greedy-heuristic","repo_url":"https://git.skewed.de/count0/graph-tool","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1310.4378","atlas_url":"https://app.syntology.ai/?focus=1310.4378","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}