Distribution-Aware Unsupervised Attacks on Graph Contrastive Learning

Graph contrastive learning has significantly advanced unsupervised graph representation learning, achieving performance comparable to supervised models. However, the robustness of graph contrastive learning models remains a major challenge. Most existing adversarial attacks are designed for supervised settings, making them inapplicable when label information is unavailable in unsupervised scenarios. To address this limitation, we propose D2AGCL, a distribution-aware unsupervised attack specifically designed for graph contrastive learning models. Our approach poisons graph data to degrade the overall quality of graph contrast learning embeddings by dynamically adjusting attack zones and gradient aggregation strategies, thus compromising the performance of down stream tasks. Extensive experiments on multiple benchmark datasets demonstrate that D2AGCL consistently outperforms existing unsupervised attack methods and even achieves comparable or superior performance against supervised adversarial baselines.