Unsupervised Multi-view Nonlinear Graph Embedding

In this paper, we study the unsupervised multi-view graph embedding (UMGE) problem, which aims to learn graph embedding from multiple perspectives in an unsupervised manner. However, the vast majority of multi-view learning work focuses on non-graph data, and surprisingly there are limited work on UMGE. By systematically analyzing different existing methods for UMGE, we discover that cross-view and nonlinearity play a vital role in efficiently improving graph embedding quality. Motivated by this concept, we develop an unsupervised Multi-viEw nonlineaR Graph Embedding (MERGE) approach to model relational multi-view consistency. Experimental results on five benchmark datasets demonstrate that MERGE significantly outperforms the state-of-the-art baselines in terms of accuracy in node classification tasks without sacrificing the computational efficiency.