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.

翻译：我们针对结构攻击下的无监督图对比学习（GCL）的对抗鲁棒性展开研究。首先，对现有攻击进行全面实证与理论分析，揭示其降低GCL性能的机理与原因。基于分析结果，我们提出一种鲁棒GCL框架，该框架整合了同质性驱动的净化视角，可同对比学习联合优化。然而，该框架面临的核心挑战在于净化目标函数不可微。为解决这一问题，我们提出系列技术以实现基于梯度的端到端鲁棒GCL。此外，我们开发了全无监督超参数调优方法，与现有方法不同，该方法无需节点标签信息。我们开展大量实验，评估所提模型GCHS（基于同质性驱动净化视角的图对比学习）在两种最新结构攻击下的表现。结果表明，在生成节点嵌入质量及两项重要下游任务性能方面，GCHS持续超越所有最先进基线模型。