Evolution Algorithm for Community Detection in Social Networks Using Node Centrality

Abstract

Community structure identification has received a great effort among computer scientists who are focusing on the properties of complex networks like the internet, social networks, food networks, e-mail networks and biochemical networks. Automatic network clustering can uncover natural groups of nodes called communities in real networks that reveals its underlying structure and functions. In this paper, we use a multiobjective evolution community detection algorithm, which forms center-based communities in a network exploiting node centrality. Node centrality is easy to use for better partitions and for increasing the convergence of evolution algorithm. The proposed algorithm reveals the center-based natural communities with high quality. Experiments on real-world networks demonstrate the efficiency of the proposed approach.