CC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks

Abstract A community structure is an integral part of a social network. Detecting such communities plays an important role in a wide range of applications, including but not limited to cluster analysis, recommendation systems and understanding the behaviour of complex systems. Researchers have derived many algorithms to discover the community structures of networks. Discovering communities is a challenging task, and there is no single algorithm that produces the best results for all networks. Therefore, despite many elegant solutions, discovering communities remains an active area of research. In this paper, we propose a novel algorithm, the Clustering Coefficient-based Genetic Algorithm (CC-GA), for detecting them in social and complex networks. Researchers have used several genetic algorithms to detect communities, but the proposed algorithm is novel in terms of both the generation of the initial population and the mutation method, and these improve its efficiency and accuracy. Experiments on a variety of real-world datasets and a comparison to state-of-the-art genetic and non-genetic-based algorithms show improved results.

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