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.

我们研究了无监督图对比学习（GCL）在结构攻击下的对抗性鲁棒性。通过全面的实证和理论分析，我们展示了现有攻击如何以及为何降低了 GCL 的性能。受到分析结果的启发，我们提出了一种鲁棒的 GCL 框架，该框架集成了基于同质性驱动的消毒视图，并可以与对比学习一起学习。然而，这也带来了一个关键挑战，即消毒目标的不可微性。为了解决这个挑战，我们提出了一系列技术以实现基于梯度的端到端鲁棒 GCL。此外，我们开发了一种完全无监督的超参数调整方法，与先前的方法不同，它不需要节点标签的知识。我们进行了大量实验证明了我们提出的 GCHS 模型（具有基于同质性驱动的消毒视图的图对比学习）在面向 GCL 的两种先进结构攻击上始终优于所有现有基准的生成节点嵌入质量以及两个重要下游任务的性能。