Improving Heterogeneous Graph Contrastive Learning Robustness via Hierarchical Vulnerability Protection

Recently, Heterogeneous Graph Contrastive Learning (HGCL) has received significant attention due to its impressive capability to represent heterogeneous graphs without detailed annotations. However, the inherent fragility of heterogeneous graph structures makes HGCL vulnerable to perturbation attacks. Most existing defense works for heterogeneous graphs primarily focus on supervised scenarios, which protect all nodes equally via structural pruning. This defensive mechanism can result in insufficient structure information for HGCL, thus degrading performance in self-supervised scenarios without labels. In this paper, we argue that some nodes are more susceptible to attacks, and the influence of the perturbation attack will accumulate across layers during representation aggregation. To tackle these problems, we propose a novel Heterogeneous Graph Contrastive Learning with Hierarchical Vulnerability Protection (HVP-HGCL), which identifies the most vulnerable nodes to perturbation attack and protects them across different aggregation layers to improve the robustness of HGCL. Specifically, we first design the Vulnerability Detection (VD) based on the HGCL framework to determine which nodes are more sensitive to attack in self-supervised scenarios. Subsequently, we propose a simple but efficient Hierarchical Protection (HP) to safeguard those vulnerable nodes from attack noise during different layers. Combining the above two modules, HVP-HGCL can not only improve the robustness of HGCL but also ensure sufficient structural information for effective contrastive learning. Extensive experiments demonstrate that HVP-HGCL improves robustness against adversarial attacks and achieves competitive performance on downstream tasks.