Abstract: The existing network representation learning algorithms mainly consist of the methods based on the shallow neural network and the approaches based on neural matrix factorization.It has been proved that network representation learning based on shallow neural network is to factorize feature matrix of network structure.In addition,most of the existing network representation algorithms learn the features from the structure information,which is a single view representation learning for networks.However,there are various kinds of views in the network.Therefore,this paper proposed a network representation learning approach based on multi-view ensemble (MVENR).The algorithm abandons the neural network training process and integrates the idea of matrix information ensemble and factorization into the network representation vectors.MVENR gives effective combination strategy between the network structure view.The link weight view and the node attribute view.Meanwhile,it makes up the shortage of neglecting the network link weight,and solves the sparse network feature problem for using single view training.The experimental results show that the proposed algorithm outperforms the commonly joint learning algorithms and the methods purely based on network structure features,and it is a simple and efficient network representation learning algorithm.

Key words: Complex network encoding learning, Network embedding learning, Network representation learning, Network visualization, Representation learning