Identifying key players in large social networks by using a multi-objective artificial bee colony optimization approach

Abstract

Identifying a set of individuals that have an influential relevance and act as key players is a matter of interest in many real world situations, especially in those related to the Internet. Although several approaches have been proposed in order to identify key players sets, they mainly focus just on the optimization of a single objective. This may lead to a poor performance since the sets identified are not usually able to perform well in real life applications where more objectives of interest are taken into account. Multi-objective optimization seems the natural extension for this task, but there is a lack of this type of methodologies in the scientific literature. An efficient Multi-Objective Artificial Bee Colony (MOABC) algorithm is proposed to address the key players identification problem and is applied in the context of six networks of different dimensions and characteristics. The proposed approach is able to best identify the key players than the ones previously proposed, especially in the context of large social networks. The model performance of the proposed approach has been evaluated according to different quality metrics. The results from the MOABC execution show important improvements with respect to the best multi-objective results in the scientific literature, specifically, in average, 13.20% of improvement in Hypervolume, 120.39% in Coverage Relation and 125.52% in number of non-dominated solutions. Even more, the proposed algorithm is also more robust when repeating executions.