Stable Communities Detection Method for Temporal Multiplex Graphs: Heterogeneous Social Network Case Study

Abstract

Abstract Multiplex graphs have been recently proposed as a model to represent high-level complexity in real-world networks such as heterogeneous social networks where actors could be characterized by heterogeneous properties and could be linked with different types of social interactions. This has brought new challenges in community detection, which aims to identify pertinent groups of nodes in a complex graph. In this context, great efforts have been made to tackle the problem of community detection in multiplex graphs. However, most of the proposed methods until recently deal with static multiplex graph and ignore the temporal dimension, which is a key characteristic of real networks. Even more, the few methods that consider temporal graphs, they just propose to follow communities over time and none of them use the temporal aspect directly to detect stable communities, which are often more meaningful in reality. Thus, this paper proposes a new two-step method to detect stable communities in temporal multiplex graphs. The first step aims to find the best static graph partition at each instant by applying a new hybrid community detection algorithm, which considers both relations heterogeneities and nodes similarities. Then, the second step considers the temporal dimension in order to find final stable communities. Finally, experiments on synthetic graphs and a real social network show that this method is competitive and it is able to extract high-quality communities.