A novel complex network community detection approach using discrete particle swarm optimization with particle diversity and mutation

Abstract Community detection in complex network is a hot issue in the field of complex network. This paper proposed a community detection approach in complex network using discrete particle swarm optimization with particle diversity and mutation (DPSO-PDM) strategy, aiming at the modularity optimization. Firstly, this paper redefined particle coding, particle velocity, particle position and evolutionary operation in discretization, which effectively solve the problem of traditional evolutionary algorithm in need of prior knowledge. Secondly, considering the traditional particle swarm optimization algorithm is easy to be trapped in the local optimum; this paper used the hybridizing inertia weight adjustment strategy based on new particle diversity and adaptive mutation strategy to avoid the local convergence of the algorithm. Finally this paper applied the DPSO-PDM algorithm to artificial baseline network data sets and real network data sets. Theoretic analysis and experimental results have shown that DPSO-PDM is effective to detect community structure with stable community division quality and global convergence in complex network.