End-to-end training with global optimization have popularized graph neural networks (GNNs) for node classification, yet inadvertently introduced vulnerabilities to adversarial edge-perturbing attacks. Adversaries can exploit the inherent opened interfaces of GNNs' input and output, perturbing critical edges and thus manipulating the classification results. Current defenses, due to their persistent utilization of global-optimization-based end-to-end training schemes, inherently encapsulate the vulnerabilities of GNNs. This is specifically evidenced in their inability to defend against targeted secondary attacks. In this paper, we propose the Graph Agent Network (GAgN) to address the aforementioned vulnerabilities of GNNs. GAgN is a graph-structured agent network in which each node is designed as an 1-hop-view agent. Through the decentralized interactions between agents, they can learn to infer global perceptions to perform tasks including inferring embeddings, degrees and neighbor relationships for given nodes. This empowers nodes to filtering adversarial edges while carrying out classification tasks. Furthermore, agents' limited view prevents malicious messages from propagating globally in GAgN, thereby resisting global-optimization-based secondary attacks. We prove that single-hidden-layer multilayer perceptrons (MLPs) are theoretically sufficient to achieve these functionalities. Experimental results show that GAgN effectively implements all its intended capabilities and, compared to state-of-the-art defenses, achieves optimal classification accuracy on the perturbed datasets.

翻译：端到端训练与全局优化使图神经网络（GNNs）在节点分类任务中广受欢迎，却也意外引入了对抗性边扰动攻击的脆弱性。攻击者可利用GNNs输入输出的固有开放接口，扰动关键边，从而篡改分类结果。现有防御方法因持续采用基于全局优化的端到端训练范式，本质上也继承了GNNs的脆弱性，具体表现为无法抵御针对性二次攻击。本文提出图代理网络（Graph Agent Network, GAgN）以解决上述GNNs的脆弱性问题。GAgN是一种图结构代理网络，其中每个节点被设计为单跳视域（1-hop-view）代理。通过代理间的去中心化交互，各代理可学习推断全局感知信息，从而执行嵌入推理、节点度及邻域关系推断等任务。这使得节点能在执行分类任务的同时过滤对抗性边。此外，代理的有限视域可阻止恶意消息在GAgN中全局传播，从而抵御基于全局优化的二次攻击。我们证明单隐藏层多层感知机（MLPs）在理论上足以实现这些功能。实验结果表明，GAgN有效实现了其所有预期能力，相较于现有最优防御方法，在扰动数据集上取得了最优分类准确率。