Recent studies show that Graph Neural Networks(GNNs) are vulnerable and
easily fooled by small perturbations, which has raised considerable concerns
for adapting GNNs in various safety-critical applications. In this work, we
focus on the emerging but critical attack, namely, Graph Injection Attack(GIA),
in which the adversary poisons the graph by injecting fake nodes instead of
modifying existing structures or node attributes. Inspired by findings that the
adversarial attacks are related to the increased heterophily on perturbed
graphs (the adversary tends to connect dissimilar nodes), we propose a general
defense framework CHAGNN against GIA through cooperative homophilous
augmentation of graph data and model. Specifically, the model generates
pseudo-labels for unlabeled nodes in each round of training to reduce
heterophilous edges of nodes with distinct labels. The cleaner graph is fed
back to the model, producing more informative pseudo-labels. In such an
iterative manner, model robustness is then promisingly enhanced. We present the
theoretical analysis of the effect of homophilous augmentation and provide the
guarantee of the proposal's validity. Experimental results empirically
demonstrate the effectiveness of CHAGNN in comparison with recent
state-of-the-art defense methods on diverse real-world datasets.

本文提出了一种防御框架 CHAGNN 以应对图注射攻击，通过合作同色增强图数据和模型，生成假标签和减少节点间异色边来提高模型鲁棒性，并通过理论分析和实验验证方法的有效性。