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FnRGNN: Distribution-aware Fairness in Graph Neural Network

arXiv 2510.19257 ↗First posted 22 Oct 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

Graph Neural Networks (GNNs) excel at learning from structured data, yet fairness in regression tasks remains underexplored. Existing approaches mainly target classification and representation-level debiasing, which cannot fully address the continuous nature of node-level regression. We propose FnRGNN, a fairness-aware in-processing framework for GNN-based node regression that applies interventions at three levels: (i) structure-level edge reweighting, (ii) representation-level alignment via MMD, and (iii) prediction-level normalization through Sinkhorn-based distribution matching. This multi-level strategy ensures robust fairness under complex graph topologies. Experiments on four real-world datasets demonstrate that FnRGNN reduces group disparities without sacrificing performance. Code is available at https://github.com/sybeam27/FnRGNN.

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References · 22

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Inductive Representation Learning on Large Graphscited by 6,320
  3. 2015Generative Moment Matching Networkscited by 257
  4. 2020Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informationcited by 168
  5. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  6. 2013Discrimination in Online Ad Deliverycited by 93
  7. 2021EDITS: Modeling and Mitigating Data Bias for Graph Neural Networkscited by 92
  8. 2020Fairness-Aware Explainable Recommendation over Knowledge Graphscited by 74
  9. 2022Learning Fair Node Representations with Graph Counterfactual Fairnesscited by 58
  10. 2020DeBayes: a Bayesian Method for Debiasing Network Embeddingscited by 55
  11. 2022RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Networkcited by 27
  12. 2023Fairness-Aware Graph Neural Networks: A Surveycited by 22
10 more
  1. 2022A Survey on Fairness for Machine Learning on Graphscited by 20
  2. 2022Template based Graph Neural Network with Optimal Transport Distancescited by 20
  3. 2022Interpreting Unfairness in Graph Neural Networks via Training Node Attributioncited by 18
  4. 2022FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editingcited by 15
  5. 2022On Structural Explanation of Bias in Graph Neural Networkscited by 15
  6. 2023Chasing Fairness in Graphs: A GNN Architecture Perspectivecited by 12
  7. 2023BeMap: Balanced Message Passing for Fair Graph Neural Networkcited by 11
  8. 2022FairSNA: Algorithmic Fairness in Social Network Analysiscited by 9
  9. 2022Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendationcited by 7
  10. 2022FairMILE: Towards an Efficient Framework for Fair Graph Representation Learningcited by 4

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