Toward Noise-Resistant Graph Embedding With Subspace Clustering Information.

Abstract

Most existing approaches of attributed network embedding often combine topology and attribute information based on the homophily assumption. In many real-world networks, such an assumption does not hold since the nodes are usually associated with many noisy or irrelevant attributes. To tackle this issue, we propose a noise-resistant graph embedding method, called NGE, by leveraging the subspace clustering information (i.e., the formation of communities is driven by different latent features in distinct subspaces). Specifically, we first construct a tensor to represent a given attributed network and then map it into different feature subspaces to capture community structure via tensor decomposition. For structure embedding, the link-level and community-level constraints are imposed. For attribute embedding, the feature-selection constraint is used to reinforce the relationship between topology and noise-removal attributes. By learning structure and attribute embedding with subspace clustering information, NGE can benefit both community detection, link prediction, and node classification. Extensive experimental results have demonstrated the superiority of NGE over many state-of-the-art approaches.