A Multi-Objective Genetic Algorithm for Community Detection in Multidimensional Social Network

Abstract

Multidimensionality in social networks is a great issue that came out into view as a result of that most social media sites such as Facebook, Twitter, and YouTube allow people to interact with each other through different social activities. The community detection in such multidimensional social networks has attracted a lot of attention in the recent years. When dealing with these networks the concept of community detection changes to be, the discovery of the shared group structure across all network dimensions such that members in the same group interact with each other more frequently than those outside the group. Most of the studies presented on the topic of community detection assume that there is only one kind of relation in the network. In this paper, we propose a multi-objective approach, named MOGA-MDNet, to discover communities in multidimensional networks, by applying genetic algorithms. The method aims to find community structure that simultaneously maximizes modularity, as an objective function, in all network dimensions. This method does not need any prior knowledge about number of communities. Experiments on synthetic and real life networks show the capability of the proposed algorithm to successfully detect the structure hidden within these networks.