  Graph Neural Networks (GNNs) have attracted substantial interest due to their
exceptional performance on graph-based data. However, their robustness,
especially on heterogeneous graphs, remains underexplored, particularly against
adversarial attacks. This paper proposes HeteroKRLAttack, a targeted evasion
black-box attack method for heterogeneous graphs. By integrating reinforcement
learning with a Top-K algorithm to reduce the action space, our method
efficiently identifies effective attack strategies to disrupt node
classification tasks. We validate the effectiveness of HeteroKRLAttack through
experiments on multiple heterogeneous graph datasets, showing significant
reductions in classification accuracy compared to baseline methods. An ablation
study underscores the critical role of the Top-K algorithm in enhancing attack
performance. Our findings highlight potential vulnerabilities in current models
and provide guidance for future defense strategies against adversarial attacks
on heterogeneous graphs.
