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Leader‐based community detection algorithm for social networks
Author(s) -
Helal Nivin A.,
Ismail Rasha M.,
Badr Nagwa L.,
Mostafa Mostafa G. M.
Publication year - 2017
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1213
Subject(s) - similarity (geometry) , computer science , cluster analysis , task (project management) , community structure , social network (sociolinguistics) , social network analysis , data science , data mining , artificial intelligence , machine learning , social media , world wide web , image (mathematics) , mathematics , engineering , systems engineering , combinatorics
Community detection has become a crucial task in social network mining. Detecting communities summarizes interactions between members for gaining deep understanding of interesting characteristics shared between members of the same community. In this research, we propose a novel community detection algorithm for the purpose of revealing and analyzing hidden similar behavior of online users. The proposed algorithm is based mainly on similar members’ actions rather than the structure similarity only for the aim of detecting communities that are closely mapped to the underlying behavioral communities in real social networks. First, leaders of the social network are discovered, then, communities are detected based on those leaders. The idea is grounded on the assumption that communities could be formed around people with great influence. Extensive experiments and analysis show the ability of the proposed algorithm to successfully detect real‐world communities with improved accuracy. WIREs Data Mining Knowl Discov 2017, 7:e1213. doi: 10.1002/widm.1213 This article is categorized under: Technologies > Structure Discovery and Clustering