An empirical study of some particle swarm optimizer variants for community detection

Abstract

Swarm based intelligent algorithms are widely used in applications of almost all domains of science and engineering. Ease and flexibility of these algorithms to fit in any application has attracted even more domains in recent years. Social computing being one such domain tries to incorporate these approaches for community detection in particular. We have proposed a method to use Particle Swarm Optimization (PSO) techniques to detect communities in social network based on common interest of individual in the network. We have performed rigorous study of four PSO variants with our approach on real data sets. We found orthogonal learning approach results quality solutions but takes reasonable computation time on all the data sets for detecting communities. Cognitive avoidance approach shows average quality solutions but interestingly takes very less computation time in contrast to orthogonal learning approach. Linear time varying approach performs poorly on both cases, while linearly varying weight along with acceleration coefficients is competitive to cognitive avoidance approach. © Springer International Publishing Switzerland 2015.