Memetic Based Online Community Detection

Abstract

Community detection is one of the most important tasks in social networks analysis. This problem becomes more challenging when the structure of the network changes during the time. It is very important to update the structures of the community in a dynamic network without time-consuming procedures. This paper suggests a hybrid evolutionary algorithm for online community detection. The proposed algorithm called Memetic Based Online Community Detection (MBOC) is based on a memetic algorithm with new genetic operators and a novel stochastic local search to assign new nodes to communities and another local search called dense search to modify communities after new assignments. The method is evaluated over several well-known benchmark networks. The results show that the proposed approach outperforms the previous methods in most cases.