  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. DefenseVGAE is trained
to reconstruct graph structure. The reconstructed adjacency matrix can reduce
the effects of adversarial perturbations and boost the performance of GCNs when
facing adversarial attacks. Our experiments on a number of datasets show the
effectiveness of the proposed method under various threat models. Under some
settings it outperforms existing defense strategies. Our code has been made
publicly available at https://github.com/zhangao520/defense-vgae.
