Adversarial Attacks on Graph Neural Networks via Meta Learning
This addresses security vulnerabilities in graph neural networks for applications like social network analysis or recommendation systems, representing a novel attack method rather than an incremental improvement.
The paper tackles the problem of robustness in graph neural networks by developing adversarial attacks that perturb the graph structure during training, using meta-gradients to optimize these perturbations, resulting in significant performance drops, such as making the networks perform worse than a baseline that ignores relational information.
Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time attacks on graph neural networks for node classification that perturb the discrete graph structure. Our core principle is to use meta-gradients to solve the bilevel problem underlying training-time attacks, essentially treating the graph as a hyperparameter to optimize. Our experiments show that small graph perturbations consistently lead to a strong decrease in performance for graph convolutional networks, and even transfer to unsupervised embeddings. Remarkably, the perturbations created by our algorithm can misguide the graph neural networks such that they perform worse than a simple baseline that ignores all relational information. Our attacks do not assume any knowledge about or access to the target classifiers.