Hybrid community detection approach in multilayer social network: Scientific collaboration recommendation case study

Abstract

Within real-world social networks people are linked with multiple types of relationships, which brings new challenges in community detection for multilayer social network where each layer represents one type of relationships. However, most of existing approaches consist on transforming the problem into a classical problem of community detection in monoplex network. In this work, we propose a new hybrid community detection approach in multilayer social networks. This approach considers simultaneously the network structure (different social connections) and the homophily of participants (similarities between users). To do so we propose a new multiplex information graph model to represent multilayer social network. Then, we adapt a combined community detection algorithm to the multiplex case. Furthermore, an example in the field of scientific collaboration recommendation is given to illustrate the practical usefulness of the proposed approach. Finally, a comparison with other community detection approaches evaluates its performance.