Multi-objective particle swarm optimization with decomposition for network community discovery

Abstract

A multi-objective particle swarm optimization with decomposition for network community discovery was proposed and the multi-objective optimization model of community discovery was constructed through comparing the optimization objectives of different community discovery algorithms in social network. The proposed algorithm adopted the Chebyshev method to decompose the multi-objective optimization problem into a number of single-objective optimization subproblems and used Particle Swarm Optimization( PSO) to discover the community structure. Moreover, a new local search based mutation strategy was put forward to improve the search efficiency and speed up convergence. The proposed algorithm overcame the defects of single objective optimization methods. The experimental results on synthetic networks and real-world networks show that the proposed algorithm can discover the community structure rapidly and accurately and reveal the hierarchical community structure.