论文标题
国防vgae：防御通过变异图自动编码器对图形数据的对抗性攻击
DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph Autoencoder
论文作者
论文摘要
图形神经网络（GNN）在图形数据上实现了出色的性能。但是，最近的作品表明，它们极易受到对抗性结构扰动的影响，这使得它们的结果不可靠。在本文中，我们提出了Defensevgae，这是一个新颖的框架，利用各种图形自动编码器（VGAES）捍卫GNNS免受此类攻击。防御vgae经过训练以重建图形结构。重建的邻接矩阵可以减少对抗性扰动的影响，并在面对对抗攻击时提高GCN的性能。我们对许多数据集的实验显示了在各种威胁模型下提出的方法的有效性。在某些情况下，它的表现优于现有的防御策略。我们的代码已在https://github.com/zhangao520/defense-vgae上公开提供。
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.
