Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization

Summary

This paper proposes AT-GSE, an adversarial training method for graph neural networks that generates perturbed graphs by maximizing a graph subspace energy (GSE) regularization term, a generalization of graph energy that measures the stability of the adjacency matrix. The authors use randomized SVD and Nyström low-rank approximation to handle local and global topology perturbations from LRBCD and PRBCD. Experiments across homophily and heterophily datasets show AT-GSE outperforms prior GNN adversarial training methods in adversarial accuracy and also achieves higher clean accuracy on unperturbed graphs.

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