FnRGNN: Distribution-aware Fairness in Graph Neural Network

📝 Original Info

• Title: FnRGNN: Distribution-aware Fairness in Graph Neural Network
• ArXiv ID: 2510.19257
• Date: 2025-10-22
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (If available, list names here.) **

📝 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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