Community detection based on human social behavior

Abstract Community structure is widespread in the form of complex networks and is important. However, research on detecting community structure (community detection) still has some unsolved problems. In this paper, we introduce a new community detection algorithm, called Commansor ( Com munity Detection Based on Hu man So cial Behavi or ), which automatically detects communities in the network by simulating human social behavior. The fundamental principle of Commansor is finding the social comfort zone of human beings in the social process and simulating the evolution of social network structure in the information spreading process. Different closeness relationships between nodes form a comfort zone for each node, and the process of information spreading has an important impact on the evolution of the community structure. The main steps of the Commansor algorithm include (a) finding a comfort zone for each node by using closeness matrices , (b) randomly selecting an associated node from the comfort zone for each node to reconstruct a simplified network, and (c) adjusting the simplified network structure by simulating the process of information spreading in human society. We compared Commansor with a range of typical algorithms by performing extensive experiments on synthetic networks and real-world networks. The experiments proved that, in most cases, our algorithm is superior to the compared algorithms in terms of the quality of community detection.