提出多层级公平性干预框架，提升图神经网络回归的公平性
FnRGNN: Distribution-aware Fairness in Graph Neural Network
- 从结构、表征、预测三层面进行公平性干预
- 在四个真实数据集上降低群体差异且不损失性能
- 适合关注图学习公平性的研究人员和应用开发者
图神经网络在结构化数据学习中表现优异，但回归任务中的公平性研究仍不足。现有方法多针对分类任务和表征层面去偏，难以应对节点回归的连续特性。本文提出FnRGNN，一种面向图神经网络节点回归的公平性感知内处理框架，通过三个层次的干预实现：(i) 结构层边权重重调，(ii) 表征层基于MMD的对齐，(iii) 预测层通过Sinkhorn算法实现分布匹配的归一化。该多层级策略在复杂图拓扑下仍能保证强公平性。在四个真实世界数据集上的实验表明，FnRGNN有效降低群体差异，同时保持模型性能。代码已公开于https://github.com/sybeam27/FnRGNN。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。