Community detection in complex networks using immune clone selection algorithm

Abstract

Based on optimization modularity, many algorithms were proposed to detect community structure in complex networks. As a optimization measure, modularity has resolution limits problems. A new measure named by modularity density was introduced, which can overcome the resolution limits drawbacks of modularity function. In this paper, we propose a immune clone selection algorithm for detecting community structure in complex networks based on optimization modularity density. Immune clone selection algorithm is a stochastic searching method. It can carry out global searching effectively. Using the global searching method of immune clone selection algorithm, experiments on synthetic and real life networks proved the proposed algorithm to be an effective community detection algorithm.