Community Detection in Complex Networks Using Link Strength-Based Hybrid Genetic Algorithm

Abstract

Communities have proven to be one of the important topological features of complex networks and can be discovered in various aspects of life. Understanding these community structures help the researchers to unlock distinct characteristics of networks that are not visible otherwise. In this paper, a hybrid genetic algorithm with link strength-based local search strategy (HGALS) is proposed for solving the community detection problem. The local search method presented in the algorithm is faster than the traditional modularity-based search operations. Furthermore, different variants of link strength measures are used in the local search method that is useful for various types of complex networks. The HGALS algorithm is analysed using different community structure metrics and its outcome is compared with three evolutionary algorithms and seven non-evolutionary algorithm-based approaches. The results thus obtained from the comparisons with other algorithms show good performances of HGALS in most of the cases for identifying better community structures.