# Adversarial Training Methods for Network Embedding

arXiv: https://arxiv.org/abs/1908.11514 · HTML: https://mlsanity.com/paper/1908.11514/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 26 of 26)

- [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)
- [Model Stealing Attacks Against Inductive Graph Neural Networks](https://mlsanity.com/paper/2112.08331/) (2021)
- [Model Inversion Attacks against Graph Neural Networks](https://mlsanity.com/paper/2209.07807/) (2022)
- [Adversarial Camouflage for Node Injection Attack on Graphs](https://mlsanity.com/paper/2208.01819/) (2022)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [Domain-adaptive Message Passing Graph Neural Network](https://mlsanity.com/paper/2308.16470/) (2023)
- [Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective](https://mlsanity.com/paper/2105.08007/) (2021)
- [GraphAttacker: A General Multi-Task GraphAttack Framework](https://mlsanity.com/paper/2101.06855/) (2021)
- [A Robust and Generalized Framework for Adversarial Graph Embedding](https://mlsanity.com/paper/2105.10651/) (2021)
- [Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks](https://mlsanity.com/paper/2312.03979/) (2023)
- [Eguard: Defending LLM Embeddings Against Inversion Attacks via Text Mutual Information Optimization](https://mlsanity.com/paper/2411.05034/) (2024)
- [Adversarial Training: A Survey](https://mlsanity.com/paper/2410.15042/) (2024)
- [IDEA: Invariant Defense for Graph Adversarial Robustness](https://mlsanity.com/paper/2305.15792/) (2023)
- [Uncertainty Quantification on Graph Learning: A Survey](https://mlsanity.com/paper/2404.14642/) (2024)
- [Exact Verification of Graph Neural Networks with Incremental Constraint Solving](https://mlsanity.com/paper/2508.09320/) (2025)
- [On Generalization of Graph Autoencoders with Adversarial Training](https://mlsanity.com/paper/2107.02658/) (2021)
- [Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses](https://mlsanity.com/paper/2508.19641/) (2025)
- [Graph Adversarial Immunization for Certifiable Robustness](https://mlsanity.com/paper/2302.08051/) (2023)
- [Graphfool: Targeted Label Adversarial Attack on Graph Embedding](https://mlsanity.com/paper/2102.12284/) (2021)
- [Adversarial Robustness of Probabilistic Network Embedding for Link Prediction](https://mlsanity.com/paper/2107.01936/) (2021)
- [Graph Convolutional Neural Networks Sensitivity under Probabilistic Error Model](https://mlsanity.com/paper/2203.07831/) (2022)
- [PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding](https://mlsanity.com/paper/2408.02705/) (2024)
- [Unifying Adversarial Perturbation for Graph Neural Networks](https://mlsanity.com/paper/2509.00387/) (2025)
- [Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality](https://mlsanity.com/paper/2107.13876/) (2021)
- [Exploring High-Order Structure for Robust Graph Structure Learning](https://mlsanity.com/paper/2203.11492/) (2022)

## Abstract (arXiv)

Network Embedding is the task of learning continuous node representations for networks, which has been shown effective in a variety of tasks such as link prediction and node classification. Most of existing works aim to preserve different network structures and properties in low-dimensional embedding vectors, while neglecting the existence of noisy information in many real-world networks and the overfitting issue in the embedding learning process. Most recently, generative adversarial networks (GANs) based regularization methods are exploited to regularize embedding learning process, which can encourage a global smoothness of embedding vectors. These methods have very complicated architecture and suffer from the well-recognized non-convergence problem of GANs. In this paper, we aim to introduce a more succinct and effective local regularization method, namely adversarial training, to network embedding so as to achieve model robustness and better generalization performance. Firstly, the adversarial training method is applied by defining adversarial perturbations in the embedding space with an adaptive L2 norm constraint that depends on the connectivity pattern of node pairs. Though effective as a regularizer, it suffers from the interpretability issue which may hinder its application in certain real-world scenarios. To improve this strategy, we further propose an interpretable adversarial training method by enforcing the reconstruction of the adversarial examples in the discrete graph domain. These two regularization methods can be applied to many existing embedding models, and we take DeepWalk as the base model for illustration in the paper. Empirical evaluations in both link prediction and node classification demonstrate the effectiveness of the proposed methods.