Incorporate Group Information to Enhance Network Embedding

The problem of representing large-scale networks with low-dimensional vectors has received considerable attention in recent years. Except the networks that include only vertices and edges, a variety of networks contain information about groups or communities. For example, on Facebook, in addition to users and the follower-followee relations between them, users can also create and join groups. However, previous studies have rarely utilized this valuable information to generate embeddings of vertices. In this paper, we investigate a novel method for learning the network embeddings with valuable group information for large-scale networks. The proposed methods take both the inner structures of the groups and the information across groups into consideration. Experimental results demonstrate that the embeddings generated by the proposed methods significantly outperform state-of-the-art network embedding methods on two different scale real-world networks.