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

arXiv: https://arxiv.org/abs/2006.08900 · HTML: https://mlsanity.com/paper/2006.08900/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Data Augmentation for Deep Graph Learning: A Survey](https://mlsanity.com/paper/2202.08235/) (2022)
- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [Graph Data Augmentation for Graph Machine Learning: A Survey](https://mlsanity.com/paper/2202.08871/) (2022)
- [Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision](https://mlsanity.com/paper/2203.03762/) (2022)
- [Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks](https://mlsanity.com/paper/2302.02829/) (2023)
- [Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks](https://mlsanity.com/paper/2301.02039/) (2023)
- [BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection](https://mlsanity.com/paper/2106.09989/) (2021)
- [Robust Graph Neural Networks using Weighted Graph Laplacian](https://mlsanity.com/paper/2208.01853/) (2022)
- [When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learning](https://mlsanity.com/paper/2409.14161/) (2024)
- [REGE: A Method for Incorporating Uncertainty in Graph Embeddings](https://mlsanity.com/paper/2412.05735/) (2024)

## Abstract (arXiv)

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.