Community Detection by Analysing Spread of Behavior in Complex Networks

Community detection is vital to analyze the structure of complex networks. However, existing community detection algorithms didn´t consider the function of the community which can always share the common interests and has more interactions amongst its members. In this paper, we presented a novel community detection method by analyzing the spread of behavior in complex networks. We experiment with several real-world networks datasets. The results show that our method is more efficient and flexible which also exhibited great improvement in classifying.