Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations
This work addresses the challenge of capturing both feature and topology information in graph learning, which is incremental as it builds on existing mutual information methods by adding multi-view integration.
The paper tackles the problem of unsupervised graph representation learning by integrating feature and topology views through mutual information maximization, achieving comparable or better performance than previous supervised methods under linear evaluation.
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.