Real-world graphs are dynamic, constantly evolving with new interactions, such as financial transactions in financial networks. Temporal Graph Neural Networks (TGNNs) have been developed to effectively capture the evolving patterns in dynamic graphs. While these models have demonstrated their superiority, being widely adopted in various important fields, their vulnerabilities against adversarial attacks remain largely unexplored. In this paper, we propose T-SPEAR, a simple and effective adversarial attack method for link prediction on continuous-time dynamic graphs, focusing on investigating the vulnerabilities of TGNNs. Specifically, before the training procedure of a victim model, which is a TGNN for link prediction, we inject edge perturbations to the data that are unnoticeable in terms of the four constraints we propose, and yet effective enough to cause malfunction of the victim model. Moreover, we propose a robust training approach T-SHIELD to mitigate the impact of adversarial attacks. By using edge filtering and enforcing temporal smoothness to node embeddings, we enhance the robustness of the victim model. Our experimental study shows that T-SPEAR significantly degrades the victim model's performance on link prediction tasks, and even more, our attacks are transferable to other TGNNs, which differ from the victim model assumed by the attacker. Moreover, we demonstrate that T-SHIELD effectively filters out adversarial edges and exhibits robustness against adversarial attacks, surpassing the link prediction performance of the naive TGNN by up to 11.2% under T-SPEAR.

本研究提出了 T-SPEAR 方法，用于针对连续时间动态图上的链接预测进行简单有效的敌对攻击，并探讨了时序图神经网络的弱点。同时，我们还提出了鲁棒的训练方法 T-SHIELD，通过边过滤和强制节点嵌入的时间平滑，提高了受攻击模型的鲁棒性。实验结果表明，T-SPEAR 显著降低了受攻击模型在链接预测任务中的性能，而且我们的攻击方法在不同于攻击者所假设的受攻击模型的 TGNN 上也具有传递性。此外，我们还证明了 T-SHIELD 能够有效过滤掉敌对边，并在面对敌对攻击时展现出强大的鲁棒性，在 T-SPEAR 方法下使链接预测性能超过了普通 TGNN 最多 11.2%。

长时动态图模型的对抗攻击和防御方法：矛与盾