Graph Neural Network Defense Combined with Contrastive Learning

Abstract

Although graph neural networks have achieved good performance in the field of graph representation learning, recent studies have shown that graph neural networks are more susceptible to adversarial attacks on graph structure, namely, by adding well-designed perturbations to the graph structure, the performance of graph neural network drops sharply. At present, although mainstream graph structure denoising methods can effectively resist graph structure adversarial attacks, due to the uncertainty of the degree of adversarial attack on the input graph, such methods are prone to more misidentifications when the input graph is not attacked or the attack intensity is small, which damages the prediction results of the graph neural network. To alleviate this problem, this paper proposes a graph neural network defense method combined with contrastive learning (CLD-GNN). Firstly, on the basis of feature similarity denoising, according to the characteristics of label inconsistency between edge endpoints after attack, the label propagation algorithm is used to obtain pseudo-labels of unlabeled nodes, and possible perturbed edges are removed according to the pseudo-label inconsistency between endpoints, resulting in the purification graph. Then, graph convolution is performed on the purification and input graph respectively. Finally, contrastive learning is applied to aligning the predicted label information on the two graphs and modifying the feature representation of the purification graph nodes. Defense experiments are conducted on 3 benchmark datasets and 2 attack scenarios for graph adversarial attacks. Experimental results show that CLD-GNN not only solves the problem of graph denoising methods and prediction effects, but also exhibits excellent defense ability.