Multi-View Attributed Network Embedding Using Manifold Regularization Preserving Non-Negative Matrix Factorization

Weiwei Yuan, Xiang Li, Donghai Guan

Abstract: Attributed network has more network information, so more and more attention is paid to the embedding of attributed network. A few existing works have considered the node attributes plays a crucial role in the quality of network embedding. They use the non-negative matrix factorization (NMF) method to mine the network information of network structure and node attributes respectively. Considering the reconstruction error of NMF method, the original network information will be lost when the final network embedding is generated. In this paper, we propose a novel multi-view attributed network embedding model with manifold regularization (Mane). The manifold regularization is added to the model to better reflect the Riemann geometry structure of the network in the feature space to enhance the information. And the problem of missing information of NMF is solved. Our approach uses the NMF to get the non-negative coefficient matrix corresponding to network structure and node attributes. Then cooperative regularization and manifold regularization is added to obtain more information in the final network embedding. The model proposed in this paper has been verified by experiments on several real data sets. The result shows that the model is superior to the state-of-the-art algorithm in node classification task.