{"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/real-time-community-detection-in-large-social","title":"Real-Time Community Detection in Large Social Networks on a Laptop","arxiv_id":"1601.03958","date":"2016-01-15","proceeding":null,"authors":["Benjamin Paul Chamberlain","Josh Levy-Kramer","Clive Humby","Marc Peter Deisenroth"],"abstract":"For a broad range of research, governmental and commercial applications it is\nimportant to understand the allegiances, communities and structure of key\nplayers in society. One promising direction towards extracting this information\nis to exploit the rich relational data in digital social networks (the social\ngraph). As social media data sets are very large, most approaches make use of\ndistributed computing systems for this purpose. Distributing graph processing\nrequires solving many difficult engineering problems, which has lead some\nresearchers to look at single-machine solutions that are faster and easier to\nmaintain. In this article, we present a single-machine real-time system for\nlarge-scale graph processing that allows analysts to interactively explore\ngraph structures. The key idea is that the aggregate actions of large numbers\nof users can be compressed into a data structure that encapsulates user\nsimilarities while being robust to noise and queryable in real-time. We achieve\nsingle machine real-time performance by compressing the neighbourhood of each\nvertex using minhash signatures and facilitate rapid queries through Locality\nSensitive Hashing. These techniques reduce query times from hours using\nindustrial desktop machines operating on the full graph to milliseconds on\nstandard laptops. Our method allows exploration of strongly associated regions\n(i.e. communities) of large graphs in real-time on a laptop. It has been\ndeployed in software that is actively used by social network analysts and\noffers another channel for media owners to monetise their data, helping them to\ncontinue to provide free services that are valued by billions of people\nglobally.","url_abs":"http://arxiv.org/abs/1601.03958v2","url_pdf":"http://arxiv.org/pdf/1601.03958v2.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":"real-time-community-detection-in-large-social","repo_url":"https://github.com/melifluos/LSH-community-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"community-detection","task_name":"Community Detection"},{"task_slug":"distributed-computing","task_name":"Distributed Computing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}