An approach for community detection in social networks based on cooperative games theory. Issue 2 (21st December 2015)

• Record Type: Journal Article
• Authors: Zhou, Lihua; Lü, Kevin; Liu, Weiyi
• Is Part Of: Expert systems. Volume 33:Issue 2(2016)
• Page Start: 176
• Page End: 188
• Publication Date: 2015-12-21
• Subjects: social networks -- community detection -- cooperative game; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12141
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Detecting communities is of great importance in social network analysis. However it is an issue that has not yet been satisfactorily solved, despite the efforts made by interdisciplinary research communities over the past few years, because of the nature of complexity in deciding how community structures should be recognized. In this paper we propose an approach based on cooperative game theory for community detection in social networks. We regard individuals as players, and regard communities as coalitions formed by players, and model community detection problem as the formation and optimization of coalitions. Furthermore, we define coalition profile for players to indicate coalitions that players joined, the order of a coalition profile is defined as the number of coalitions in a coalition profile, and we introduce a utility function to measure preference of coalition profiles. Accordingly, we propose an algorithm to detect a coalition profile with maximal utility function values. We have implemented the algorithms developed in this study and experimental results demonstrate the effectiveness of our approaches.