Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations

TL;DR

This paper introduces a novel unsupervised graph representation learning method that maximizes mutual information across feature and topology views, improving the capture of comprehensive graph information.

Contribution

It proposes a multi-view learning framework with mutual information maximization and a disagreement regularization to enhance graph representations.

Findings

Effective in capturing both local and global information.

Achieves comparable or better performance than supervised methods.

Demonstrates success on synthetic and real-world datasets.

Abstract

Recently, maximizing mutual information has emerged as a powerful method for unsupervised graph representation learning. The existing methods are typically effective to capture information from the topology view but ignore the feature view. To circumvent this issue, we propose a novel approach by exploiting mutual information maximization across feature and topology views. Specifically, we first utilize a multi-view representation learning module to better capture both local and global information content across feature and topology views on graphs. To model the information shared by the feature and topology spaces, we then develop a common representation learning module using mutual information maximization and reconstruction loss minimization. To explicitly encourage diversity between graph representations from the same view, we also introduce a disagreement regularization to enlarge the distance between representations from the same view. Experiments on synthetic and real-world datasets demonstrate the effectiveness of integrating feature and topology views. In particular, compared with the previous supervised methods, our proposed method can achieve comparable or even better performance under the unsupervised representation and linear evaluation protocol.