A Memetic Particle Swarm Optimization Algorithm for Community Detection in Complex Networks
In recent years, community detection has become a hot research topic in complex networks. Many of the proposed algorithms are for detecting community based on the modularity Q. However, there is a resolution limit problem in modularity optimization methods. In order to detect the community structure more effectively, a memetic particle swarm optimization algorithm (MPSOA) is proposed to optimize the modularity density by introducing particle swarm optimization based global search operator and tabu local search operator, which is useful to keep a balance between diversity and convergence. For comparison purposes, two state of the art algorithms, namely, meme net and fast modularity, are carried on the synthetic networks and other four real world network problems. The obtained experiment results show that the proposed MPSOA is an efficient heuristic approach for the community detection problems.
Int. J. Pattern Recognit. Artif. Intell.