Team Formation in Social Networks Using Imperialist Competitive Algorithm

Abstract

With the prevalence of various social sites and the rapid development of Internet communication, the problem of team formation in social networks has aroused the enthusiasm of many scholars. Previous research concentrates on raising the variants of this problem and most of them rely on designing approximation optimization algorithms, whose disadvantage is lacking extensibility. In this paper, we deal with the team formation problem for finding a cooperative team within a social network to perform a specific task that requires a set of skills and minimizing the communication cost among team members. In the light of its NP-hard nature, Imperialist Competitive Algorithm (ICA), an evolutionary algorithm for optimization inspired by the imperialistic competition, has been utilized for this problem with different ways of measuring the communication cost. We design a discrete version of ICA by introducing genetic operator in our application, imperialist mutation and imperialist crossover with similarity detection are proposed to avoid a local optimum. Comprehensive experiments on a real-world dataset indicate the performance of the ICA algorithm obtains high-quality results with the comparison of some state-of-the-art ones.