Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs

arXiv:2308.10779v2 Announce Type: replace
Abstract: 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, …

abstract adversarial adversarial attacks arxiv attacks continuous cs.lg cs.si defense dynamic financial graph graph neural networks graphs interactions link prediction networks neural networks patterns prediction temporal transactions type world