Attacking Graph Convolutional Networks via Rewiring
This addresses security concerns for users of GNNs in graph-related tasks, but is incremental as it builds on existing adversarial attack methods.
The paper tackles the vulnerability of Graph Neural Networks (GNNs) to adversarial attacks by proposing a graph rewiring operation that is less noticeable than adding or deleting edges, and uses reinforcement learning to learn attack strategies, demonstrating effectiveness on real-world graphs.
Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification. Recent researches show that graph neural networks are vulnerable to adversarial attacks, which deliberately add carefully created unnoticeable perturbation to the graph structure. The perturbation is usually created by adding/deleting a few edges, which might be noticeable even when the number of edges modified is small. In this paper, we propose a graph rewiring operation which affects the graph in a less noticeable way compared to adding/deleting edges. We then use reinforcement learning to learn the attack strategy based on the proposed rewiring operation. Experiments on real world graphs demonstrate the effectiveness of the proposed framework. To understand the proposed framework, we further analyze how its generated perturbation to the graph structure affects the output of the target model.