DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph Autoencoder

Graph neural networks (GNNs) achieve remarkable performance for tasks on graph data. However, recent works show they are extremely vulnerable to adversarial structural perturbations, making their outcomes unreliable. In this paper, we propose DefenseVGAE, a novel framework leveraging variational graph autoencoders (VGAEs) to defend GNNs against such attacks.