A Unified Model for Community Detection of Multiplex Networks

Abstract

Multiplex networks contain multiple simplex networks. Community detection of multiplex networks needs to deal with information from all the simplex networks. Most approaches aggregate all the links in different simplex networks treating them as being equivalent. However, such aggregation might ignore information of importance in simplex networks. In addition, for each simplex network, the aggregation only considers adjacency relation among nodes, which can’t reflect real closeness among nodes very well. In order to solve the problems above, this paper presents a unified model to detect community structure by grouping the nodes based on a unified matrix transferred from multiplex network. In particular, we define importance and node similarity to describe respectively correlation difference of simplex networks and closeness among nodes in each simplex network. The experiment results show the higher accuracy of our model for community detection compared with competing methods on synthetic datasets and real world datasets.