An Artificial Bee Colony (ABC) Algorithm for Efficient Partitioning of Social Networks

Abstract

Social networks clustering is an NP-hard problem because it is difficult to find the communities in a reasonable time; therefore, the solutions are based on heuristics. Social networks clustering aims to collect people with common interest in one group. Several approaches have been developed for clustering social networks. In this paper the researchers, introduce a new approach to cluster social networks based on Artificial Bee Colony optimization algorithm, which is a swarm based meta-heuristic algorithm. This approach aims to maximize the modularity, which is a measure that represents the quality of network partitioning. The researchers cluster some real known social networks with the proposed algorithm and compare it with the other approaches. Their algorithm increases the modularity and gives higher quality solutions than the previous approaches.