Single Node Adversarial Attack Against Graph Neural Network
Graph neural networks(GNNs)have shown excellent performance in a variety of graph-related applications.Re-cent studies show that GNN models are vulnerable to carefully construct adversarial perturbations,resulting in degraded model per-formance.Most of the previous research on graph adversarial attacks focus on modifying the graph structure,which will change the important topology properties of the graph.Indirect adversarial attacks on graph data are studied,and a single node adversarial at-tack(SNAA)based on reinforcement learning to modify the node features in graphs is proposed.The attack is set in a black-box scenario,where only a limited number of black-box queries can be performed on the test data,and the attack budget is strictly limit-ed to ensure that the attack is imperceptible.Experiments on multiple datasets show that SNAA is effective against various GNN models.
graph neural networkgraph adversarial attackreinforcement learningadversarial example
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