Despite its success in the image domain, adversarial training does not (yet)
stand out as an effective defense for Graph Neural Networks (GNNs) against
graph structure perturbations. In the pursuit of fixing adversarial training
(1) we show and overcome fundamental theoretical as well as practical
limitations of the adopted graph learning setting in prior work; (2) we reveal
that more flexible GNNs based on learnable graph diffusion are able to adjust
to adversarial perturbations, while the learned message passing scheme is
naturally interpretable; (3) we introduce the first attack for structure
perturbations that, while targeting multiple nodes at once, is capable of
handling global (graph-level) as well as local (node-level) constraints.
Including these contributions, we demonstrate that adversarial training is a
state-of-the-art defense against adversarial structure perturbations.

本文介绍了对抗训练在解决图神经网络中的结构扰动方面的局限并展示了基于可学习图扩散的灵活图神经网络和针对多节点和全局约束的结构扰动攻击，进一步证明了对抗训练作为对抗性结构扰动的最先进防御手段。