# Fast Gradient Attack on Network Embedding

arXiv: https://arxiv.org/abs/1809.02797 · HTML: https://mlsanity.com/paper/1809.02797/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2018.

## Built on by (most cited first, 30 of 65)

- [A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability](https://mlsanity.com/paper/2204.08570/) (2022)
- [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)
- [Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning](https://mlsanity.com/paper/2111.04314/) (2021)
- [Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection](https://mlsanity.com/paper/2306.00006/) (2023)
- [On the Robustness of Graph Neural Diffusion to Topology Perturbations](https://mlsanity.com/paper/2209.07754/) (2022)
- [A Hard Label Black-box Adversarial Attack Against Graph Neural Networks](https://mlsanity.com/paper/2108.09513/) (2021)
- [Model Inversion Attacks against Graph Neural Networks](https://mlsanity.com/paper/2209.07807/) (2022)
- [Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation](https://mlsanity.com/paper/2201.07986/) (2022)
- [Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach](https://mlsanity.com/paper/2310.06396/) (2023)
- [Revisiting Robustness in Graph Machine Learning](https://mlsanity.com/paper/2305.00851/) (2023)
- [Jointly Attacking Graph Neural Network and its Explanations](https://mlsanity.com/paper/2108.03388/) (2021)
- [Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning](https://mlsanity.com/paper/2110.06468/) (2021)
- [Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND](https://mlsanity.com/paper/2404.17099/) (2024)
- [Graph Structural Attack by Perturbing Spectral Distance](https://mlsanity.com/paper/2111.00684/) (2021)
- [CogDL: A Comprehensive Library for Graph Deep Learning](https://mlsanity.com/paper/2103.00959/) (2021)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection](https://mlsanity.com/paper/2208.06776/) (2022)
- [GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections](https://mlsanity.com/paper/2210.12598/) (2022)
- [Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees](https://mlsanity.com/paper/2205.03546/) (2022)
- [Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training](https://mlsanity.com/paper/2311.15772/) (2023)
- [Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study](https://mlsanity.com/paper/2401.04331/) (2024)
- [GraphAttacker: A General Multi-Task GraphAttack Framework](https://mlsanity.com/paper/2101.06855/) (2021)
- [Simple and Efficient Partial Graph Adversarial Attack: A New Perspective](https://mlsanity.com/paper/2308.07834/) (2023)
- [HGAttack: Transferable Heterogeneous Graph Adversarial Attack](https://mlsanity.com/paper/2401.09945/) (2024)
- [Sparse Vicious Attacks on Graph Neural Networks](https://mlsanity.com/paper/2209.09688/) (2022)
- [HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks](https://mlsanity.com/paper/2302.12407/) (2023)
- [Node Copying: A Random Graph Model for Effective Graph Sampling](https://mlsanity.com/paper/2208.02435/) (2022)
- [SAGMAN: Stability Analysis of Graph Neural Networks on the Manifolds](https://mlsanity.com/paper/2402.08653/) (2024)
- [Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges](https://mlsanity.com/paper/2412.18365/) (2024)

## Abstract (arXiv)

Network embedding maps a network into a low-dimensional Euclidean space, and thus facilitate many network analysis tasks, such as node classification, link prediction and community detection etc, by utilizing machine learning methods. In social networks, we may pay special attention to user privacy, and would like to prevent some target nodes from being identified by such network analysis methods in certain cases. Inspired by successful adversarial attack on deep learning models, we propose a framework to generate adversarial networks based on the gradient information in Graph Convolutional Network (GCN). In particular, we extract the gradient of pairwise nodes based on the adversarial network, and select the pair of nodes with maximum absolute gradient to realize the Fast Gradient Attack (FGA) and update the adversarial network. This process is implemented iteratively and terminated until certain condition is satisfied, i.e., the number of modified links reaches certain predefined value. Comprehensive attacks, including unlimited attack, direct attack and indirect attack, are performed on six well-known network embedding methods. The experiments on real-world networks suggest that our proposed FGA behaves better than some baseline methods, i.e., the network embedding can be easily disturbed using FGA by only rewiring few links, achieving state-of-the-art attack performance.