Multi-view network embedding with node similarity ensemble

Node similarity is utilized as the most popular guidance for network embedding: nodes more similar in a network should still be more similar when mapping node information from a high-dimensional vector space to a low-dimensional vector space. Most existing methods preserve a single node similarity in the network embedding, which can merely preserve one-side network structural information. Though some works try to utilize several node similarities to preserve more network information, they fail to consider the interrelationships between the latent spaces preserving different node similarities. This causes both network information insufficiency and network information redundancy. To solve the problems of existing works, we propose a novel multi-view network embedding model with node similarity ensemble. Node similarities are first selected to maximize the represented network information while minimizing the information redundancy. For each combination of the selected node similarities, a latent space is generated as a view of the network. A Canonical Correlation Analysis based approach is then used to extract the common structure of the latent spaces alignment, and a neural network based approach is used to extract the view-specific latent structure by measuring the asymmetric KL divergence of nodes’ Gaussian distribution. The common structure and the view-specific structure of multiple views are merged to perverse the overall network information. Experiments held on the real-world networks verified the superiority of the proposed method to existing works.