A novel algorithm for community detection based on resistance distance and similarity

Abstract

Community detection is important for understanding the structure and function of networks. Resistance distance is a kind of distance function inherent in the network itself, which has important applications in many fields. In this paper, we propose a novel community detection algorithm based on resistance distance and similarity. First, we propose the node similarity, which is based on the common nodes and resistance distance. Then, we define the distance function between nodes by similarity. Furthermore, we calculate the distance between communities by using the distance between nodes. Finally, we detect the community structure in the network according to the nearest-neighbor nodes being in the same community. Experimental results on artificial networks and real-world networks show that the proposed algorithm can effectively detect the community structures in complex networks.