A fast simulated annealing strategy for community detection in complex networks

Abstract

Many complex networks display community structure—group of nodes within which connections are dense but between which they are sparser. A quantitative measure called modularity (Q) has been proposed to effectively assess the quality of community structure. Many community detection methods based on Q have been proposed. However, they have low accuracy or time consuming. In this paper, we suggest a fast simulated annealing method (FSA) to detect communities. An initial community partition is first obtained accord to similarity metric and then the FSA method is used to optimize the Q. Experimental results on real and synthetic networks show that compared with the existing simulated annealing method (SA), FSA method can not only maintain the quality of community but also improve the efficiency greatly.