{"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/linearized-and-single-pass-belief-propagation","title":"Linearized and Single-Pass Belief Propagation","arxiv_id":"1406.7288","date":"2014-06-27","proceeding":null,"authors":["Wolfgang Gatterbauer","Stephan Günnemann","Danai Koutra","Christos Faloutsos"],"abstract":"How can we tell when accounts are fake or real in a social network? And how\ncan we tell which accounts belong to liberal, conservative or centrist users?\nOften, we can answer such questions and label nodes in a network based on the\nlabels of their neighbors and appropriate assumptions of homophily (\"birds of a\nfeather flock together\") or heterophily (\"opposites attract\"). One of the most\nwidely used methods for this kind of inference is Belief Propagation (BP) which\niteratively propagates the information from a few nodes with explicit labels\nthroughout a network until convergence. One main problem with BP, however, is\nthat there are no known exact guarantees of convergence in graphs with loops.\n  This paper introduces Linearized Belief Propagation (LinBP), a linearization\nof BP that allows a closed-form solution via intuitive matrix equations and,\nthus, comes with convergence guarantees. It handles homophily, heterophily, and\nmore general cases that arise in multi-class settings. Plus, it allows a\ncompact implementation in SQL. The paper also introduces Single-pass Belief\nPropagation (SBP), a \"localized\" version of LinBP that propagates information\nacross every edge at most once and for which the final class assignments depend\nonly on the nearest labeled neighbors. In addition, SBP allows fast incremental\nupdates in dynamic networks. Our runtime experiments show that LinBP and SBP\nare orders of magnitude faster than standard","url_abs":"http://arxiv.org/abs/1406.7288v4","url_pdf":"http://arxiv.org/pdf/1406.7288v4.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":"linearized-and-single-pass-belief-propagation","repo_url":"https://github.com/sslh/sslh","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"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}