Adversarial attacks against dynamic graph neural networks via node injection

Abstract

Dynamic graph neural networks (DGNNs) have demonstrated their extraordinary value in many practical applications. Nevertheless, the vulnerability of DNNs is a serious hidden danger as a small disturbance added to the model can markedly reduce its performance. At the same time, current adversarial attack schemes are implemented on static graphs, and the variability of attack models prevents these schemes from transferring to dynamic graphs. In this paper, we use the diffused attack of node injection to attack the DGNNs, and first propose the node injection attack based on structural fragility against DGNNs, named Structural Fragility-based Dynamic Graph Node Injection Attack (SFIA). SFIA firstly determines the target time based on the period weight. Then, it introduces a structural fragile edge selection strategy to establish the target nodes set and link them with the malicious node using serial inject. Finally, an optimization function is designed to generate adversarial features for malicious nodes. Experiments on datasets from four different fields show that SFIA is significantly superior to many comparative approaches. When the graph is injected with 1% of the original total number of nodes through SFIA, the link prediction Recall and MRR of the target DGNN link decrease by 17.4% and 14.3% respectively, and the accuracy of node classification decreases by 8.7%.

Keywords

• Dynamic graph neural network
• Adversarial attack
• Malicious node
• Vulnerability