Estimation of Distribution Algorithm with Local Sampling Strategy for Community Detection in Complex Networks

摘要

It is important to discover the potential community structure for analyzing complex networks. In this paper, an estimation of distribution algorithm with local sampling strategy for community detection in complex networks is presented to optimize the modularity density function. In the proposed algorithm, the evolution probability model is built according to eminent individuals selected by simulated annealing mechanism and a local sampling strategy based on a local similarity model is adopted to improve both the speed and the accuracy for detecting community structure in complex networks. At the same time, a more general version of the criterion function with a tunable parameter. lambda is used to avoid the resolution limit. Experiments on synthetic and real-life networks demonstrate the performance and the comparison of experimental results with those of several state-of-the-art methods, the proposed algorithm is considerably efficient and competitive.