Graph Convolutional Networks based Non-Negative Matrix Factorization aware Community detection

Abstract

Nonnegative matrix factorization (NMF) is widely used in community discovery because of its effectiveness and easy interpretability. However, most of the existing NMF-based community detection methods are linear. They cannot effectively deal with the nonlinear characteristics of complex networks, resulting in further improvement in community detection performance. Aiming at this problem, a convolution graph network (GCN) enhanced nonlinear NMF community discovery method NMFGCN is proposed. NMFGCN consists of two main modules: GCN and NMF, where GCN is used to learn network node representations, and NMF uses node representations as input to obtain network community representations. In addition, a joint optimization method is proposed to train NMFGCN, which enables NMFGCN to have nonlinear feature representation capabilities and enables GCN and NMF to promote each other and obtain better community segmentation results. Many experiments on artificial synthetic networks and entire networks show that NMFGCN is superior to current NMF-based community discovery methods , thus proving that NMFGCN can improve the performance of NMF community discovery methods. In addition, NMFGCN also outperforms Deep Walk and LINE standard graph representation learning methods.