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cs.LG2024
Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
Yuxiang Zhang, Xin Liu, Meng Wu +4
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 a…
cs.LG2024★ 1 cited
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
Xin Liu, Yuxiang Zhang, Meng Wu +6
Edge perturbation is a basic method to modify graph structures. It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i.e.…