most citedCAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks

3 citations · 3 across the 1 of their papers we have counts for

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cs.LG2021★ 3 cited

CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks

Haotian Xue, Kaixiong Zhou, Tianlong Chen +4

Despite the recent advances of graph neural networks (GNNs) in modeling graph data, the training of GNNs on large datasets is notoriously hard due to the overfitting. Adversarial t…