Graph Neural Networks (GNNs) have been successfully exploited in graph analysis tasks in many real-world applications. However, GNNs have been shown to have potential security issues imposed by adversarial samples generated by attackers, which achieved great attack performance with almost imperceptible perturbations. What limit the wide application of these attackers are their methods' specificity on a certain graph analysis task, such as node classification or link prediction. We thus propose GraphAttacker, a novel generic graph attack framework that can flexibly adjust the structures and the attack strategies according to the graph analysis tasks. Based on the Generative Adversarial Network (GAN), GraphAttacker generates adversarial samples through alternate training on three key components, the Multi-strategy Attack Generator (MAG), the Similarity Discriminator (SD), and the Attack Discriminator(AD). Furthermore, to achieve attackers within perturbation budget, we propose a novel Similarity Modification Rate (SMR) to quantify the similarity between nodes thus constrain the attack budget. We carry out extensive experiments and the results show that GraphAttacker can achieve state-of-the-art attack performance on graph analysis tasks of node classification, graph classification, and link prediction. Besides, we also analyze the unique characteristics of each task and their specific response in the unified attack framework. We will release GraphAttacker as an open-source simulation platform for future attack researches.

本研究提出了一种名为GraphAttacker的新型通用图形攻击框架，该框架可以根据图形分析任务灵活调整结构和攻击策略，并且能够在没有进行对抗训练的情况下，在节点分类、图分类和链接预测等图形分析任务中实现最先进的攻击性能。

GraphAttacker: 一个通用的多任务图攻击框架