AGSOA:Graph Neural Network Targeted Attack Based on Average Gradient and Structure Optimization

Abstract

Graph Neural Networks(GNNs) are vulnerable to adversarial attack that cause performance degradation by adding small perturbations to the graph. Gradient-based attacks are one of the most commonly used methods and have achieved good performance in many attack scenarios. However, current gradient attacks face the problems of easy to fall into local optima and poor attack invisibility. Specifically, most gradient attacks use greedy strategies to generate perturbations, which tend to fall into local optima leading to underperformance of the attack. In addition, many attacks only consider the effectiveness of the attack and ignore the invisibility of the attack, making the attacks easily exposed leading to failure. To address the above problems, this paper proposes an attack on GNNs, called AGSOA, which consists of an average gradient calculation and a structre optimization module. In the average gradient calculation module, we compute the average of the gradient information over all moments to guide the attack to generate perturbed edges, which stabilizes the direction of the attack update and gets rid of undesirable local maxima. In the structure optimization module, we calculate the similarity and homogeneity of the target node's with other nodes to adjust the graph structure so as to improve the invisibility and transferability of the attack. Extensive experiments on three commonly used datasets show that AGSOA improves the misclassification rate by 2$\%$-8$\%$ compared to other state-of-the-art models.