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

3 citations · 6 across the 2 of their papers we have counts for

collaborators

2 papers

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…

cs.LG2021★ 3 cited

Orthogonal Graph Neural Networks

Kai Guo, Kaixiong Zhou, Xia Hu +3

Graph neural networks (GNNs) have received tremendous attention due to their superiority in learning node representations. These models rely on message passing and feature transfor…