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A Distributed Hybrid Community Detection Methodology for Social Networks

Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece
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Algorithms 2019, 12(8), 175; https://doi.org/10.3390/a12080175
Received: 30 June 2019 / Revised: 26 July 2019 / Accepted: 15 August 2019 / Published: 17 August 2019
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–Newman’s 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. View Full-Text
Keywords: community detection; distributed computing; social networks; node attributes; homophily community detection; distributed computing; social networks; node attributes; homophily
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Georgiou, K.; Makris, C.; Pispirigos, G. A Distributed Hybrid Community Detection Methodology for Social Networks. Algorithms 2019, 12, 175.

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