Abstract: How to detect community in a multiplex network is a knotty problem. Currently some algorithms represent the multiplex network as a three-way tensor and use non-negative tensor factorization to capture the community structure. However, if there are many edges between communities or when the multiplex network is sparse, the non-negative tensor factorization algorithm won’t work well. To this end, this paper introduced an improved algorithm. The algorithm first merges the layers which have strong correlation to reduce the number of layers of multiplex network for the sake of highlighting the community structure. And then the algorithm uses non-negative tensor factorization to detect community. This paper validates the approach on both synthetic benchmarks and real multiplex networks, and the result shows that the algorithm performs better than the old approach.

Key words: multiplex network, community detection, non-negative tensor factorization