Learning unsupervised node representation from multi-view network

This paper studies the problem of learning node representations for networks with multiple views, which aims to infer robust node representations by simultaneously considering multiple views during the representations learning process. We propose an effective method for this task, named as Multi-View Representation Learning (MVRL). The new method extends the matrix factorization model for node representation learning of multi-view network in unsupervised representation learning scenario, which simultaneously learns a set of view weights to identify the quality of each view, and the network representations as matrix factorization of the weighted combination of multiple views. An efficient optimization method with linear complexity is proposed to solve the new model, and a simple yet efficient method is proposed for fast updating of the new node's vector representation without updating the whole nodes’ representation vectors. We have evaluated the performance of our proposed approach on five real-world multi-view network datasets. Experimental results on the node classification task demonstrated the superior performance and efficiency of our proposed method.