Manifold Approximation and Projection by Maximizing Graph Information

Graph representation learning is an effective method to represent graph data in a low dimensional space, which facilitates graph analytic tasks. The existing graph representation learning algorithms suffer from certain constraints. Random walk based methods and graph convolutional neural networks, tend to capture graph local information and fail to preserve global structural properties of graphs. We present MAPPING (Manifold APproximation and Projection by maximizINg Graph information), an unsupervised deep efficient method for learning node representations, which is capable of synchronously capturing both local and global structural information of graphs. In line with applying graph convolutional networks to construct initial representation, the proposed approach employs an information maximization process to attain representations to capture global graph structures. Furthermore, in order to preserve graph local information, we extend a novel manifold learning technique to the field of graph learning. The output of MAPPING can be easily exploited by downstream machine learning models on graphs. We demonstrate our competitive performance on three citation benchmarks. Our approach outperforms the baseline methods significantly