A memetic algorithm using local structural information for detecting community structure in complex networks

Caihong Mu, Jin Xie, Ruochen Liu, Licheng Jiao

Abstract: Community detection has received a great deal of attention in recent years. Modularity is the most used and best known quality function for measuring the quality of a partition of a network. Based on the optimization of modularity, we proposed a memetic algorithm with a local search operator to detect community structure. The local search operator uses a quality function of local community tightness based on structural similarity. In addition, the tactics of vertex mover is used for reassigning vertices to neighboring communities to improve the partition result. Experiments on real-world networks and computer-generated networks show the effectiveness of our algorithm.