基于子图采样的大规模图对抗性攻击方法

为提高对抗性攻击在大规模图上的攻击效率,提出了基于子图采样的对抗样本生成方法. 该方法通过引入PageRank、余弦相似度及K跳子图等技术,提取与目标节点高度相关的子图,在大规模图上缓解了计算梯度效率较低的问题,在降低被攻击模型准确性的同时提升了攻击的隐蔽性. 实验结果表明： 所提出的对抗性攻击方法与基于梯度攻击的GradArgmax算法相比,在Cora数据集上提升了30.7%的攻击性能,且在Reddit大规模数据上能够计算GradArgmax算法无法计算的攻击扰动.

Subgraph sampling-based adversarial attack method for large-scale graphs

A subgraph sampling-based adversarial example generation method was proposed to enhance the efficiency of adversarial attacks on large-scale graphs. PageRank, cosine similarity, and K-hop subgraphs were employed to extract subgraphs highly relevant to the target node in this method, alleviating the issue of low gradient computation efficiency in large-scale graphs. The stealthiness of the attack was also increased while reducing the accuracy of the attacked model. Experimental results showed that attack performance was improved by 30.7% on the Cora dataset by this adversarial attack method compared to the GradArgmax algorithm, and attack perturbations on large-scale like Reddit dataset could be computed which the GradArgmax algorithm could not achieve.