# Can Adversarial Network Attack be Defended?

arXiv: https://arxiv.org/abs/1903.05994 · HTML: https://mlsanity.com/paper/1903.05994/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2019.

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

- [A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability](https://mlsanity.com/paper/2204.08570/) (2022)
- [Graph Data Augmentation for Graph Machine Learning: A Survey](https://mlsanity.com/paper/2202.08871/) (2022)
- [Spectral Adversarial Training for Robust Graph Neural Network](https://mlsanity.com/paper/2211.10896/) (2022)
- [GraphAttacker: A General Multi-Task GraphAttack Framework](https://mlsanity.com/paper/2101.06855/) (2021)
- [Learning Robust Representation through Graph Adversarial Contrastive Learning](https://mlsanity.com/paper/2201.13025/) (2022)
- [Uncertainty Quantification on Graph Learning: A Survey](https://mlsanity.com/paper/2404.14642/) (2024)
- [On Generalization of Graph Autoencoders with Adversarial Training](https://mlsanity.com/paper/2107.02658/) (2021)
- [Spatio-Temporal Sparsification for General Robust Graph Convolution Networks](https://mlsanity.com/paper/2103.12256/) (2021)
- [Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution](https://mlsanity.com/paper/2201.04728/) (2022)
- [Perseus: Leveraging Common Data Patterns with Curriculum Learning for More Robust Graph Neural Networks](https://mlsanity.com/paper/2410.12425/) (2024)

## Abstract (arXiv)

Machine learning has been successfully applied to complex network analysis in various areas, and graph neural networks (GNNs) based methods outperform others. Recently, adversarial attack on networks has attracted special attention since carefully crafted adversarial networks with slight perturbations on clean network may invalid lots of network applications, such as node classification, link prediction, and community detection etc. Such attacks are easily constructed with serious security threat to various analyze methods, including traditional methods and deep models. To the best of our knowledge, it is the first time that defense method against network adversarial attack is discussed. In this paper, we are interested in the possibility of defense against adversarial attack on network, and propose defense strategies for GNNs against attacks. First, we propose novel adversarial training strategies to improve GNNs' defensibility against attacks. Then, we analytically investigate the robustness properties for GNNs granted by the use of smooth defense, and propose two special smooth defense strategies: smoothing distillation and smoothing cross-entropy loss function. Both of them are capable of smoothing gradient of GNNs, and consequently reduce the amplitude of adversarial gradients, which benefits gradient masking from attackers. The comprehensive experiments show that our proposed strategies have great defensibility against different adversarial attacks on four real-world networks in different network analyze tasks.