mlsanityData to 7 Oct 2026

Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections

arXiv 2406.03052 ↗First posted 5 Jun 2024cs.LGcited by 2

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Abstract (arXiv)

Despite the remarkable capabilities demonstrated by Graph Neural Networks (GNNs) in graph-related tasks, recent research has revealed the fairness vulnerabilities in GNNs when facing malicious adversarial attacks. However, all existing fairness attacks require manipulating the connectivity between existing nodes, which may be prohibited in reality. To this end, we introduce a Node Injection-based Fairness Attack (NIFA), exploring the vulnerabilities of GNN fairness in such a more realistic setting. In detail, NIFA first designs two insightful principles for node injection operations, namely the uncertainty-maximization principle and homophily-increase principle, and then optimizes injected nodes' feature matrix to further ensure the effectiveness of fairness attacks. Comprehensive experiments on three real-world datasets consistently demonstrate that NIFA can significantly undermine the fairness of mainstream GNNs, even including fairness-aware GNNs, by injecting merely 1% of nodes. We sincerely hope that our work can stimulate increasing attention from researchers on the vulnerability of GNN fairness, and encourage the development of corresponding defense mechanisms. Our code and data are released at: https://github.com/CGCL-codes/NIFA.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 34 of its 51 references that are in the map. First posted on arXiv 5 Jun 2024.

Builds on

34 of 51 references.

References · 34

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
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  4. 2019Simplifying Graph Convolutional Networkscited by 1,470
  5. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  6. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 395
  7. 2020GNNGuard: Defending Graph Neural Networks against Adversarial Attackscited by 195
  8. 2020Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informationcited by 168
  9. 2018NetGAN: Generating Graphs via Random Walkscited by 129
  10. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  11. 2021EDITS: Modeling and Mitigating Data Bias for Graph Neural Networkscited by 92
  12. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
22 more
  1. 2021GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophilycited by 84
  2. 2021User-oriented Fairness in Recommendationcited by 75
  3. 2021TDGIA:Effective Injection Attacks on Graph Neural Networkscited by 67
  4. 2021Elastic Graph Neural Networkscited by 67
  5. 2022Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakagecited by 61
  6. 2020Poisoning Attacks on Algorithmic Fairnesscited by 58
  7. 2020Scalable Attack on Graph Data by Injecting Vicious Nodescited by 50
  8. 2020Exacerbating Algorithmic Bias through Fairness Attackscited by 47
  9. 2021Single Node Injection Attack against Graph Neural Networkscited by 45
  10. 2019A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learningcited by 30
  11. 2021Unbiased Graph Embedding with Biased Graph Observationscited by 28
  12. 2023On Generalized Degree Fairness in Graph Neural Networkscited by 28
  13. 2024FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralizationcited by 20
  14. 2022Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learningcited by 19
  15. 2023The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillationcited by 16
  16. 2022Robust Fair Clustering: A Novel Fairness Attack and Defense Frameworkcited by 13
  17. 2022Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networkscited by 12
  18. 2022ChemicalX: A Deep Learning Library for Drug Pair Scoringcited by 11
  19. 2021EqGNN: Equalized Node Opportunity in Graphscited by 7
  20. 2023Fairness-aware Message Passing for Graph Neural Networkscited by 7
  21. 2023Deceptive Fairness Attacks on Graphs via Meta Learningcited by 6
  22. 2023Adversarial Attacks on Fairness of Graph Neural Networkscited by 4

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challengescited by 29
  2. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0