A three-stage algorithm on community detection in social networks

Abstract

Detecting communities or clusters of networks is a considerable interesting problem in various fields and interdisciplinary subjects in recent years. Tens of hundreds of methods with significant efforts devoted to community detection in networks, while an open problem in all methods is the unknown number of communities in real networks. It is believed that the central node in a community might be highly surrounded by its neighbors and any two centers of the community reside far from each other, and also believed the similarity among nodes in the same community is larger than the others. Therefore, the local and the global structures’ information shed important light on community detection. In this work, we present a three-stage algorithm to detect communities based on the local and the global information without giving the number of communities beforehand. The three stages include the central nodes identification, the label propagation and the communities combination. The central nodes are identified according to the distance between them larger than the average; the label propagation is to label nodes with the same colors when they reach to the maximum similarity; the communities combination is to merge two communities into one if the increment of the modularity is positive and maximum when the two communities were combined. Experiments and simulation results both on real world and synthetic networks show that the three-stage algorithm possesses well matched properties compared with seven other widely used algorithms, which indicates that three-stage algorithm can be used to detect community in social networks.