Homophily-Driven Sanitation View for Robust Graph Contrastive Learning
Yulin Zhu Xing Ai Yevgeniy Vorobeychik Kai Zhou
We investigate adversarial robustness of unsupervised Graph Contrastive
Learning (GCL) against structural attacks. First, we provide a comprehensive
empirical and theoretical analysis of existing attacks, revealing how and why
they downgrade the performance of GCL. Inspired by our analytic results, we
present a robust GCL framework that integrates a homophily-driven sanitation
view, which can be learned jointly with contrastive learning. A key challenge
this poses, however, is the non-differentiable nature of the sanitation
objective. To address this challenge, we propose a series of techniques to
enable gradient-based end-to-end robust GCL. Moreover, we develop a fully
unsupervised hyperparameter tuning method which, unlike prior approaches, does
not require knowledge of node labels. We conduct extensive experiments to
evaluate the performance of our proposed model, GCHS (Graph Contrastive
Learning with Homophily-driven Sanitation View), against two state of the art
structural attacks on GCL. Our results demonstrate that GCHS consistently
outperforms all state of the art baselines in terms of the quality of generated
node embeddings as well as performance on two important downstream tasks.