Heterogeneous Graph Neural Networks (HGNNs) are increasingly recognized for
their performance in areas like the web and e-commerce, where resilience
against adversarial attacks is crucial. However, existing adversarial attack
methods, which are primarily designed for homogeneous graphs, fall short when
applied to HGNNs due to their limited ability to address the structural and
semantic complexity of HGNNs. This paper introduces HGAttack, the first
dedicated gray box evasion attack method for heterogeneous graphs. We design a
novel surrogate model to closely resemble the behaviors of the target HGNN and
utilize gradient-based methods for perturbation generation. Specifically, the
proposed surrogate model effectively leverages heterogeneous information by
extracting meta-path induced subgraphs and applying GNNs to learn node
embeddings with distinct semantics from each subgraph. This approach improves
the transferability of generated attacks on the target HGNN and significantly
reduces memory costs. For perturbation generation, we introduce a
semantics-aware mechanism that leverages subgraph gradient information to
autonomously identify vulnerable edges across a wide range of relations within
a constrained perturbation budget. We validate HGAttack's efficacy with
comprehensive experiments on three datasets, providing empirical analyses of
its generated perturbations. Outperforming baseline methods, HGAttack
demonstrated significant efficacy in diminishing the performance of target HGNN
models, affirming the effectiveness of our approach in evaluating the
robustness of HGNNs against adversarial attacks.

HGAttack 是针对异构图设计的首个灰盒逃避攻击方法，通过设计一种新颖的代理模型，利用梯度方法实现扰动生成，提高了生成攻击在目标 HGNN 上的可转移性并显著降低了内存成本。在三个数据集上的综合实验证实了 HGAttack 的有效性。