Graph Neural Networks (GNNs) have become widely used in the field of graph
mining. However, these networks are vulnerable to structural perturbations.
While many research efforts have focused on analyzing vulnerability through
poisoning attacks, we have identified an inefficiency in current attack losses.
These losses steer the attack strategy towards modifying edges targeting
misclassified nodes or resilient nodes, resulting in a waste of structural
adversarial perturbation. To address this issue, we propose a novel attack loss
framework called the Cost Aware Poisoning Attack (CA-attack) to improve the
allocation of the attack budget by dynamically considering the classification
margins of nodes. Specifically, it prioritizes nodes with smaller positive
margins while postponing nodes with negative margins. Our experiments
demonstrate that the proposed CA-attack significantly enhances existing attack
strategies

我们提出了一种名为成本感知中毒攻击（CA-attack）的新型攻击损失框架，通过动态考虑节点的分类间距来改善攻击预算的分配，具体地，它优先处理具有较小正间距的节点，推后处理具有负间距的节点。我们的实验证明，所提出的 CA-attack 显著增强了现有的攻击策略。