Graph Neural Networks (GNNs) have established themselves as a key component
in addressing diverse graph-based tasks. Despite their notable successes, GNNs
remain susceptible to input perturbations in the form of adversarial attacks.
This paper introduces an innovative approach to fortify GNNs against
adversarial perturbations through the lens of contractive dynamical systems.
Our method introduces graph neural layers based on differential equations with
contractive properties, which, as we show, improve the robustness of GNNs. A
distinctive feature of the proposed approach is the simultaneous learned
evolution of both the node features and the adjacency matrix, yielding an
intrinsic enhancement of model robustness to perturbations in the input
features and the connectivity of the graph. We mathematically derive the
underpinnings of our novel architecture and provide theoretical insights to
reason about its expected behavior. We demonstrate the efficacy of our method
through numerous real-world benchmarks, reading on par or improved performance
compared to existing methods.

这篇研究论文通过提出了一种新颖的方法来增强图神经网络对于恶意扰动的鲁棒性，该方法基于具有收缩性质的微分方程的图神经层，同时学习节点特征和邻接矩阵的进化，从而在输入特征和图的连通性的扰动方面增强模型的鲁棒性，并通过多个真实世界基准测试验证了我们方法的有效性和性能优化。