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Adversarial attacks on Graph Neural Networks (GNNs) have emerged as a significant threat to the security of graph learning. Compared with Graph Modification Attacks (GMAs), Graph Injection Attacks (GIAs) are considered more realistic attacks, in which attackers perturb GNN models by injecting a small number of fake nodes. However, most existing black-box GIA methods either require comprehensive knowledge of the dataset and the ground-truth labels or a large number of queries to execute the attack, which is often unfeasible in many scenarios. In this paper, we propose an unsupervised method for leveraging the rich knowledge contained in the graph data themselves to enhance the success rate of graph injection attacks on the initial query. Specifically, we introduce GraphContrastive Learning-based Graph Injection Attack (GCIA), which consists of a node encoder, a reward predictor, and a fake node generator. The Graph Contrastive Learning (GCL)-based node encoder transforms nodes for low-dimensional continuous embedding, the reward predictor acts as a simplified surrogate for the target model, and the fake node generator produces fake nodes and edges based on several carefully designed loss functions, utilizing the node encoder and reward predictor. Extensive results demonstrate that the proposed GCIA method achieves a first query success rate of 91.2% on the Reddit dataset and improves the success rate to over 99.7% after 10 queries. [ABSTRACT FROM AUTHOR]

In recent years, Graph Neural Networks (GNNs) have achieved excellent applications in classification or prediction tasks. Recent studies have demonstrated that GNNs are vulnerable to adversarial attacks. Graph Modification Attack (GMA) and Graph Injection Attack (GIA) are commonly attack strategies. Most graph adversarial attack methods are based on GMA, which has a clear drawback: the attacker needs high privileges to modify the original graph, making it difficult to execute in practice. GIA can perform attacks without modifying the original graph. However, many GIA models fail to take care of attack invisibility, i.e., fake nodes can be easily distinguished from the original nodes. To solve the above issue, we propose an imperceptible graph injection attack, named IMGIA. Specifically, IMGIA uses the normal distribution sampling and mask learning to generate fake node features and links respectively, and then uses the homophily unnoticeability constraint to improve the camouflage of the attack. Our extensive experiments on three benchmark datasets demonstrate that IMGIA performs better than the existing state-of-the-art GIA methods. As an example, IMGIA shows an improvement in performance with an average increase in effectiveness of 2%. [ABSTRACT FROM AUTHOR]

Adversarial attacks on Graph Neural Networks (GNNs) have emerged as a significant threat to the security of graph learning. Compared with Graph Modification Attacks (GMAs), Graph Injection Attacks (GIAs) are considered more realistic attacks, in which attackers perturb GNN models by injecting a small number of fake nodes. However, most existing black-box GIA methods either require comprehensive knowledge of the dataset and the ground-truth labels or a large number of queries to execute the attack, which is often unfeasible in many scenarios. In this paper, we propose an unsupervised method for leveraging the rich knowledge contained in the graph data themselves to enhance the success rate of graph injection attacks on the initial query. Specifically, we introduce GraphContrastive Learning-based Graph Injection Attack (GCIA), which consists of a node encoder, a reward predictor, and a fake node generator. The Graph Contrastive Learning (GCL)-based node encoder transforms nodes for low-dimensional continuous embedding, the reward predictor acts as a simplified surrogate for the target model, and the fake node generator produces fake nodes and edges based on several carefully designed loss functions, utilizing the node encoder and reward predictor. Extensive results demonstrate that the proposed GCIA method achieves a first query success rate of 91.2% on the Reddit dataset and improves the success rate to over 99.7% after 10 queries.

Abstract In recent years, Graph Neural Networks (GNNs) have achieved excellent applications in classification or prediction tasks. Recent studies have demonstrated that GNNs are vulnerable to adversarial attacks. Graph Modification Attack (GMA) and Graph Injection Attack (GIA) are commonly attack strategies. Most graph adversarial attack methods are based on GMA, which has a clear drawback: the attacker needs high privileges to modify the original graph, making it difficult to execute in practice. GIA can perform attacks without modifying the original graph. However, many GIA models fail to take care of attack invisibility, i.e., fake nodes can be easily distinguished from the original nodes. To solve the above issue, we propose an imperceptible graph injection attack, named IMGIA. Specifically, IMGIA uses the normal distribution sampling and mask learning to generate fake node features and links respectively, and then uses the homophily unnoticeability constraint to improve the camouflage of the attack. Our extensive experiments on three benchmark datasets demonstrate that IMGIA performs better than the existing state-of-the-art GIA methods. As an example, IMGIA shows an improvement in performance with an average increase in effectiveness of 2%.