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

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

URL: http://arxiv.org/abs/2006.08900v1
Date: Tue, 16 Jun 2020 03:30:23 GMT
Title: DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph Autoencoder
Authors: Ao Zhang and Jinwen Ma
Abstract summary: Graph neural networks (GNNs) achieve remarkable performance for tasks on graph data. Recent works show they are extremely vulnerable to adversarial structural perturbations, making their outcomes unreliable. We propose DefenseVGAE, a novel framework leveraging variational graph autoencoders(VGAEs) to defend GNNs against such attacks.
Score: 22.754141951413786
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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