A Dynamic Community Discovery Method via Fusing Node Change Information
Dynamic community discovery aims to detect community structure in dynamic complex networks,and has important research value for revealing the functions and evolution patterns of networks.Because the community structure of the adjacent snapshot networks is smooth,the community discovery result of the previous snapshot network can be used to supervise the community discovery process of the current snapshot network.However,existing methods are difficult to ef-fectively extract these information to improve the performance of dynamic community discovery.In view of this,a dynamic community discovery method named NCI-SeNMF(Semi-supervised Nonnegative Matrix Factorization combining Node Change Information)is proposed,which can fuse node change information.NCI-SeNMF firstly uses k-core analysis method to extract the degeneracy core of every community network at the previous snapshot,and selects the nodes in the degenera-cy core to construct the prior community membership information.Then,it quantifies the change degree of the local topolo-gy structure of the nodes in the adjacent snapshot networks,and applies it to further improve the prior community member-ship information.Finally,it integrates the prior community membership information through semi-supervised nonnegative matrix factorization model to discover dynamic communities.Extensive comparative experiments have been conducted on several synthetic and real-world dynamic networks,and the results show that NCI-SeNMF improves at least 4.8％in term of core evaluation metrics comparing with the existing dynamic community discovery methods.
dynamic community discoverysemi-supervised nonnegative matrix factorizationk-core analysiscom-munity networkcomplex networks
万方数据
维普
