An efficient Genetic Algorithm with Modified Crossover Operator for Community Detection in Social Networks

The study of community structure in social networks has become a popular topic in recent years. Many methods for detecting communities from networks have been proposed. Since the appearance of the modularity, many authors started to see community detection as an optimization problem. they often use genetic algorithm as an effective optimization technique to solve this kind of problem. But the existing algorithms based on GA have some drawbacks such as slow convergence and low accuracy. In this paper we propose a novel community detection algorithm based on genetic algorithm. To better adapt GAs to community detection problem a novel crossover operator has been designed. The proposed algorithm does not require any prior knowledge about the network. The experimental results reveal that our algorithm performs better than most existing techniques.