# MGA: Momentum Gradient Attack on Network

arXiv: https://arxiv.org/abs/2002.11320 · HTML: https://mlsanity.com/paper/2002.11320/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

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

- [Robustness of Graph Neural Networks at Scale](https://mlsanity.com/paper/2110.14038/) (2021)
- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [A Hard Label Black-box Adversarial Attack Against Graph Neural Networks](https://mlsanity.com/paper/2108.09513/) (2021)
- [Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks](https://mlsanity.com/paper/2209.05957/) (2022)
- [GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections](https://mlsanity.com/paper/2210.12598/) (2022)
- [Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning](https://mlsanity.com/paper/2305.02901/) (2023)
- [Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation](https://mlsanity.com/paper/2211.08068/) (2022)
- [Momentum Gradient-based Untargeted Attack on Hypergraph Neural Networks](https://mlsanity.com/paper/2310.15656/) (2023)
- [AGSOA:Graph Neural Network Targeted Attack Based on Average Gradient and Structure Optimization](https://mlsanity.com/paper/2406.13228/) (2024)
- [Structural Adversarial Attacks on Relational Deep Learning under Integrity Constraints](https://mlsanity.com/paper/2607.07089/) (2026)

## Abstract (arXiv)

The adversarial attack methods based on gradient information can adequately find the perturbations, that is, the combinations of rewired links, thereby reducing the effectiveness of the deep learning model based graph embedding algorithms, but it is also easy to fall into a local optimum. Therefore, this paper proposes a Momentum Gradient Attack (MGA) against the GCN model, which can achieve more aggressive attacks with fewer rewiring links. Compared with directly updating the original network using gradient information, integrating the momentum term into the iterative process can stabilize the updating direction, which makes the model jump out of poor local optimum and enhance the method with stronger transferability. Experiments on node classification and community detection methods based on three well-known network embedding algorithms show that MGA has a better attack effect and transferability.