As the study of graph neural networks becomes more intensive and comprehensive, their robustness and security have received great research interest. The existing global attack methods treat all nodes in the graph as their attack targets. Although existing methods have achieved excellent results, there is still considerable space for improvement. The key problem is that the current approaches rigidly follow the definition of global attacks. They ignore an important issue, i.e., different nodes have different robustness and are not equally resilient to attacks. From a global attacker's view, we should arrange the attack budget wisely, rather than wasting them on highly robust nodes. To this end, we propose a totally new method named partial graph attack (PGA), which selects the vulnerable nodes as attack targets. First, to select the vulnerable items, we propose a hierarchical target selection policy, which allows attackers to only focus on easy-to-attack nodes. Then, we propose a cost-effective anchor-picking policy to pick the most promising anchors for adding or removing edges, and a more aggressive iterative greedy-based attack method to perform more efficient attacks. Extensive experimental results demonstrate that PGA can achieve significant improvements in both attack effect and attack efficiency compared to other existing graph global attack methods.

翻译：随着图神经网络研究的深入与全面，其鲁棒性和安全性引起了广泛研究兴趣。现有的全局攻击方法将图中所有节点作为攻击目标。尽管现有方法取得了优异结果，但仍存在相当大的改进空间。关键在于当前方法僵化地遵循全局攻击的定义，忽视了一个重要问题：不同节点具有不同的鲁棒性，并非所有节点都能同等抵御攻击。从全局攻击者的视角出发，我们应合理分配攻击预算，而非将其浪费在高度鲁棒的节点上。为此，我们提出了一种全新方法——局部图攻击（PGA），该方法选择脆弱节点作为攻击目标。首先，为选择脆弱节点，我们提出了一种分层目标选择策略，使攻击者仅关注易攻击节点。接着，我们提出了一种成本有效的锚点选择策略，以选取最有潜力的锚点进行边添加或移除，并设计了一种更激进的迭代贪婪攻击方法以实现更高效的攻击。大量实验结果表明，与现有其他图全局攻击方法相比，PGA在攻击效果和攻击效率上均能实现显著提升。