FnRGNN: Distribution-aware Fairness in Graph Neural Network

• URL: http://arxiv.org/abs/2510.19257v1
• Date: Wed, 22 Oct 2025 05:29:43 GMT
• Title: FnRGNN: Distribution-aware Fairness in Graph Neural Network
• Authors: Soyoung Park, Sungsu Lim,
• Abstract summary: Graph Neural Networks (GNNs) excel at learning from structured data, yet fairness in regression tasks remains underexplored.<n>We propose FnRGNN, a fairness-aware in-processing framework for GNN-based node regression.
• Score: 4.013463458124476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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