Community Detection Based on Differential Evolution Using Modularity Density

Currently, many community detection methods are proposed in the network science field. However, most contemporary methods only employ modularity to detect communities, which may not be adequate to represent the real community structure of networks for its resolution limit problem. In order to resolve this problem, we put forward a new community detection approach based on a differential evolution algorithm (CDDEA), taking into account modularity density as an optimized function. In the CDDEA, a new tuning parameter is used to recognize different communities. The experimental results on synthetic and real-world networks show that the proposed algorithm provides an effective method in discovering community structure in complex networks.