Disttack: Graph Adversarial Attacks Toward Distributed GNN Training

Yuxiang Zhang, Xin Liu, Meng Wu, Wei Yan, Mingyu Yan, Xiaochun Ye, Dongrui Fan

Abstract: Graph Neural Networks (GNNs) have emerged as potent models for graph learning. Distributing the training process across multiple computing nodes is the most promising solution to address the challenges of ever-growing real-world graphs. However, current adversarial attack methods on GNNs neglect the characteristics and applications of the distributed scenario, leading to suboptimal performance and inefficiency in attacking distributed GNN training.