An enhanced evolutionary approach for solving the community detection problem

Community detection concepts can be encountered in many disciplines such as sociology, biology, and computer science, etc. Nowadays, a huge amount of data is produced by digital social networks and needs to be processed. In fact, the analysis of this data makes it possible to extract new knowledge about groups of individuals, their communication modes, and orientations. This knowledge can be exploited in marketing, security, Web usage, and many other decisional purposes. Community detection problem (CDP) is NP-hard and many algorithms have been designed to solve it but not to a satisfactory level. In this paper, we propose a hybrid heuristic approach based on a combination of genetic algorithms and tabu search that does not need any prior knowledge about the number or the size of each community to tackle the CDP. The method is efficient because it uses an enhanced encoding, which excludes redundant chromosomes while performing genetic operations. This approach is evaluated on a wide range of real-world networks. The result of experiments shows that the proposed algorithm outperforms many other algorithms according to the modularity (Q) measure.

Abstract licence: CC BY-NC

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