# Node Injection Attacks on Graphs via Reinforcement Learning

arXiv: https://arxiv.org/abs/1909.06543 · HTML: https://mlsanity.com/paper/1909.06543/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2019.

## Built on by (most cited first, 15 of 15)

- [Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem](https://mlsanity.com/paper/2106.10785/) (2021)
- [Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning](https://mlsanity.com/paper/2211.10782/) (2022)
- [Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias](https://mlsanity.com/paper/2304.00010/) (2023)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [Simple and Efficient Partial Graph Adversarial Attack: A New Perspective](https://mlsanity.com/paper/2308.07834/) (2023)
- [HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks](https://mlsanity.com/paper/2302.12407/) (2023)
- [Adversarial Attacks on Fairness of Graph Neural Networks](https://mlsanity.com/paper/2310.13822/) (2023)
- [GUAP: Graph Universal Attack Through Adversarial Patching](https://mlsanity.com/paper/2301.01731/) (2023)
- [A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks](https://mlsanity.com/paper/2412.00789/) (2024)
- [Graphfool: Targeted Label Adversarial Attack on Graph Embedding](https://mlsanity.com/paper/2102.12284/) (2021)
- [GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization](https://mlsanity.com/paper/2408.10948/) (2024)
- [Black-box Node Injection Attack for Graph Neural Networks](https://mlsanity.com/paper/2202.09389/) (2022)
- [Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning](https://mlsanity.com/paper/2411.02809/) (2024)
- [Exploiting Meta-Learning-based Poisoning Attacks for Graph Link Prediction](https://mlsanity.com/paper/2504.06492/) (2025)
- [Navigating the Black Box: Leveraging LLMs for Effective Text-Level Graph Injection Attacks](https://mlsanity.com/paper/2506.13276/) (2025)

## Abstract (arXiv)

Real-world graph applications, such as advertisements and product recommendations make profits based on accurately classify the label of the nodes. However, in such scenarios, there are high incentives for the adversaries to attack such graph to reduce the node classification performance. Previous work on graph adversarial attacks focus on modifying existing graph structures, which is infeasible in most real-world applications. In contrast, it is more practical to inject adversarial nodes into existing graphs, which can also potentially reduce the performance of the classifier. In this paper, we study the novel node injection poisoning attacks problem which aims to poison the graph. We describe a reinforcement learning based method, namely NIPA, to sequentially modify the adversarial information of the injected nodes. We report the results of experiments using several benchmark data sets that show the superior performance of the proposed method NIPA, relative to the existing state-of-the-art methods.