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Most real-world social networks are inherently dynamic and composed of communities that are constantly changing in membership. As a result, recent years have witnessed increased attention toward the challenging problem of detecting evolving communities. This paper presents a gametheoretic approach for community detection in dynamic social networks in which each node is treated as a rational agent who periodically chooses from a set of predefined actions in order to maximize its utilitydoi:10.1109/asonam.2014.6921567 dblp:conf/asunam/AlvariHS14 fatcat:orshkdrdtrcbxjpsy76s4mje2m