A Community Detection Algorithm Of Multiplex Networks With Layer Reduction

Many complex systems in reality can be represented as networks in which node denotes entity and edge denotes connection.Multiplex network is consist of some single networks and there are two main kinds of multiplex networks.One kind multiplex network comprises many different types links that each type of link constructs one single layer.Another is temporay network of which each layer represents the links between nodes at a certain point.How to detect communities in a multiplex network is a knotty problem.Currently there are many algortihms for single network.But these algorithms can not apply to multiplex network.Some algorithms represent the multiplex network as a three-way tensor and then use non-negative tensor factorization to capture the community structure.This paper shows that 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 introduces 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 making the network more dense and 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.