{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:43:21Z","timestamp":1760240601301,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,8,17]],"date-time":"2019-08-17T00:00:00Z","timestamp":1566000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union (European Social Fund) and Greek national funds","award":["the Operational Program \u201cResearch and Innovation Strategies for Smart Specialization - RIS3\u201d of \"Partnership Agreement (PA) 2014-2020."],"award-info":[{"award-number":["the Operational Program \u201cResearch and Innovation Strategies for Smart Specialization - RIS3\u201d of \"Partnership Agreement (PA) 2014-2020."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Nowadays, the amount of digitally available information has tremendously grown, with real-world data graphs outreaching the millions or even billions of vertices. Hence, community detection, where groups of vertices are formed according to a well-defined similarity measure, has never been more essential affecting a vast range of scientific fields such as bio-informatics, sociology, discrete mathematics, nonlinear dynamics, digital marketing, and computer science. Even if an impressive amount of research has yet been published to tackle this NP-hard class problem, the existing methods and algorithms have virtually been proven inefficient and severely unscalable. In this regard, the purpose of this manuscript is to combine the network topology properties expressed by the loose similarity and the local edge betweenness, which is a currently proposed Girvan\u2013Newman\u2019s edge betweenness measure alternative, along with the intrinsic user content information, in order to introduce a novel and highly distributed hybrid community detection methodology. The proposed approach has been thoroughly tested on various real social graphs, roundly compared to other classic divisive community detection algorithms that serve as baselines and practically proven exceptionally scalable, highly efficient, and adequately accurate in terms of revealing the subjacent network hierarchy.<\/jats:p>","DOI":"10.3390\/a12080175","type":"journal-article","created":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T06:10:14Z","timestamp":1566195014000},"page":"175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Distributed Hybrid Community Detection Methodology for Social Networks"],"prefix":"10.3390","volume":"12","author":[{"given":"Konstantinos","family":"Georgiou","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece"}]},{"given":"Christos","family":"Makris","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece"}]},{"given":"Georgios","family":"Pispirigos","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.physrep.2009.11.002","article-title":"Community detection in graphs","volume":"486","author":"Fortunato","year":"2010","journal-title":"Phys. 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