mlsanityData to 7 Oct 2026

Deceptive Fairness Attacks on Graphs via Meta Learning

arXiv 2310.15653 ↗First posted 24 Oct 2023cs.LG cs.SI stat.MLcited by 6

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Abstract (arXiv)

We study deceptive fairness attacks on graphs to answer the following question: How can we achieve poisoning attacks on a graph learning model to exacerbate the bias deceptively? We answer this question via a bi-level optimization problem and propose a meta learning-based framework named FATE. FATE is broadly applicable with respect to various fairness definitions and graph learning models, as well as arbitrary choices of manipulation operations. We further instantiate FATE to attack statistical parity and individual fairness on graph neural networks. We conduct extensive experimental evaluations on real-world datasets in the task of semi-supervised node classification. The experimental results demonstrate that FATE could amplify the bias of graph neural networks with or without fairness consideration while maintaining the utility on the downstream task. We hope this paper provides insights into the adversarial robustness of fair graph learning and can shed light on designing robust and fair graph learning in future studies.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 40 of its 55 references that are in the map. First posted on arXiv 24 Oct 2023.

Builds on

40 of 55 references.

References · 40

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  5. 2014Certifying and removing disparate impactcited by 715
  6. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 395
  7. 2018Adversarial Attack on Graph Structured Datacited by 300
  8. 2019Adversarial Attacks on Graph Neural Networks via Meta Learningcited by 261
  9. 2019Topology Attack and Defense for Graph Neural Networks: An Optimization Perspectivecited by 196
  10. 2018Delayed Impact of Fair Machine Learningcited by 183
  11. 2020Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informationcited by 168
  12. 2020A Semi-supervised Graph Attentive Network for Financial Fraud Detectioncited by 150
28 more
  1. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  2. 2019Compositional Fairness Constraints for Graph Embeddingscited by 126
  3. 2018Adversarial Attacks on Node Embeddings via Graph Poisoningcited by 109
  4. 2017A Tutorial on Kernel Density Estimation and Recent Advancescited by 100
  5. 2021Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Datacited by 86
  6. 2016Hiding Individuals and Communities in a Social Networkcited by 77
  7. 2022Fairness in Graph Mining: A Surveycited by 76
  8. 2021FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learningcited by 68
  9. 2020Poisoning Attacks on Algorithmic Fairnesscited by 58
  10. 2020Exacerbating Algorithmic Bias through Fairness Attackscited by 47
  11. 2019Toward a better trade-off between performance and fairness with kernel-based distribution matchingcited by 36
  12. 2019MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancycited by 35
  13. 2021CrossWalk: Fairness-enhanced Node Representation Learningcited by 34
  14. 2021Unbiased Graph Embedding with Biased Graph Observationscited by 28
  15. 2023On Generalized Degree Fairness in Graph Neural Networkscited by 28
  16. 2022RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Networkcited by 27
  17. 2019Group-Fairness in Influence Maximizationcited by 21
  18. 2022Fairness Amidst Non-IID Graph Data: A Literature Reviewcited by 20
  19. 2022A Survey on Fairness for Machine Learning on Graphscited by 20
  20. 2018Data Poisoning Attack against Unsupervised Node Embedding Methodscited by 19
  21. 2020Exploring Algorithmic Fairness in Robust Graph Covering Problemscited by 19
  22. 2021Poisoning Attacks on Fair Machine Learningcited by 19
  23. 2022Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiencycited by 19
  24. 2021Graph Structural Attack by Perturbing Spectral Distancecited by 16
  25. 2023Explaining and Adapting Graph Conditional Shiftcited by 14
  26. 2022Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networkscited by 12
  27. 2021Fairness Degrading Adversarial Attacks Against Clustering Algorithmscited by 5
  28. 2023Everything Perturbed All at Once: Enabling Differentiable Graph Attackscited by 2

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 2.

6 papers.

  1. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  2. 2024Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attackcited by 2
  3. 2024Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injectionscited by 2
  4. 2023Fair Graph Machine Learning under Adversarial Missingness Processescited by 0
  5. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  6. 2025Adversarial Bias: Data Poisoning Attacks on Fairnesscited by 0