# Data Poisoning Attack against Unsupervised Node Embedding Methods

arXiv: https://arxiv.org/abs/1810.12881 · HTML: https://mlsanity.com/paper/1810.12881/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2018.

## Built on by (most cited first, 19 of 19)

- [Model Stealing Attacks Against Inductive Graph Neural Networks](https://mlsanity.com/paper/2112.08331/) (2021)
- [Data Poisoning Attacks Against Multimodal Encoders](https://mlsanity.com/paper/2209.15266/) (2022)
- [Reinforcement learning on graphs: A survey](https://mlsanity.com/paper/2204.06127/) (2022)
- [Universal Spectral Adversarial Attacks for Deformable Shapes](https://mlsanity.com/paper/2104.03356/) (2021)
- [Get a Model! Model Hijacking Attack Against Machine Learning Models](https://mlsanity.com/paper/2111.04394/) (2021)
- [Two-in-One: A Model Hijacking Attack Against Text Generation Models](https://mlsanity.com/paper/2305.07406/) (2023)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [Deceptive Fairness Attacks on Graphs via Meta Learning](https://mlsanity.com/paper/2310.15653/) (2023)
- [Minimum Topology Attacks for Graph Neural Networks](https://mlsanity.com/paper/2403.02723/) (2024)
- [EDoG: Adversarial Edge Detection For Graph Neural Networks](https://mlsanity.com/paper/2212.13607/) (2022)
- [Mondrian: Prompt Abstraction Attack Against Large Language Models for Cheaper API Pricing](https://mlsanity.com/paper/2308.03558/) (2023)
- [Random Walks for Adversarial Meshes](https://mlsanity.com/paper/2202.07453/) (2022)
- [Towards Resilient Artificial Intelligence: Survey and Research Issues](https://mlsanity.com/paper/2109.08904/) (2021)
- [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)
- [Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks](https://mlsanity.com/paper/2205.14440/) (2022)
- [GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models](https://mlsanity.com/paper/2311.16139/) (2023)
- [Vera Verto: Multimodal Hijacking Attack](https://mlsanity.com/paper/2408.00129/) (2024)
- [Model Hijacking Attack in Federated Learning](https://mlsanity.com/paper/2408.02131/) (2024)

## Abstract (arXiv)

Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have attracted growing interests given their simplicity and effectiveness. However, although these methods have been proved effective in a variety of applications, none of the existing work has analyzed the robustness of them. This could be very risky if these methods are attacked by an adversarial party. In this paper, we take the task of link prediction as an example, which is one of the most fundamental problems for graph analysis, and introduce a data positioning attack to node embedding methods. We give a complete characterization of attacker's utilities and present efficient solutions to adversarial attacks for two popular node embedding methods: DeepWalk and LINE. We evaluate our proposed attack model on multiple real-world graphs. Experimental results show that our proposed model can significantly affect the results of link prediction by slightly changing the graph structures (e.g., adding or removing a few edges). We also show that our proposed model is very general and can be transferable across different embedding methods. Finally, we conduct a case study on a coauthor network to better understand our attack method.