Deep Streaming Graph Representations

Abstract

Learning graph representations generally indicate mapping the vertices of a graph into a low-dimension space, in which the proximity of the original data can be preserved in the latent space. However, traditional methods that based on adjacent matrix suffered from high computational cost when encountering large graphs. In this paper, we propose a deep autoencoder driven streaming methods to learn low-dimensional representations for graphs. The proposed method process the graph as a data stream fulfilled by sampling strategy to avoid straight computation over the large adjacent matrix. Moreover, a graph regularized deep autoencoder is employed in the model to keep different aspects of proximity information. The regularized framework is able to improve the representation power of learned features during the learning process. We evaluate our method in clustering task by the features learned from our model. Experiments show that the proposed method achieves competitive results comparing with methods that directly apply deep models over the complete graphs.