Graph Contrastive Learning via Interventional View Generation

Zengyi Wo, Minglai Shao, Wenjun Wang, Xuan Guo, Lu Lin

Keywords: Graph Contrastive Learning, Heterophilic Graph Learning, Counterfactual Analysis

Abstract:

Graph contrastive learning (GCL), as a popular self-supervised learning technique, has demonstrated promising capability in learning discriminative representations for diverse downstream tasks. A large body of GCL frameworks mainly work on graphs formed under homophily effect, i.e., similar nodes tend to connect with each other. In their design, the augmentation and aggregation are usually conducted indiscriminately on edges, ignoring the existence of heterophilic edges that connect dissimilar nodes. Therefore, the efficacy of GCL could greatly deteriorate on heterophilic graphs, verified by our analysis: GCL on a mixture of homophilic and heterophilic edges will generate representations that are indistinguishable across different classes in the embedding space.
To address this challenge, we propose a novel GCL framework via interventional view generation. Specifically, we generate homophilic and heterophilic views through counterfactual intervention, which targets on disentangling homophilic and heterophilic structure from the original graph, such that we can capture their corresponding information using separate filters in the contrastive learning process. Since the homophilic view and the heterophilic view present different frequency signals, they are further encoded via a low-pass and a high-pass filter respectively. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our design. Our proposed framework achieves a remarkably improved downstream performance on graphs with high heterophily while maintaining a comparable ability in learning homophilic graphs. A comprehensive study also verifies the necessity of individual designs in our framework.

Track: Graph Algorithms and Learning for the Web

Submission Guidelines Scope: Yes

Submission Guidelines Blind: Yes

Submission Guidelines Format: Yes

Submission Guidelines Limit: Yes

Submission Guidelines Authorship: Yes

Student Author: Yes

Submission Number: 2264