{"ID":2851519,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.19257","arxiv_id":"2510.19257","title":"FnRGNN: Distribution-aware Fairness in Graph Neural Network","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.19257","url_pdf":"https://arxiv.org/pdf/2510.19257v1","authors":"[\"Soyoung Park\",\"Sungsu Lim\"]","published":"2025-10-22T05:29:43Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Graph Neural Network\"]","has_code":false,"code_links":[{"ID":607910,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2851519,"paper_url":"https://arxiv.org/abs/2510.19257","paper_title":"FnRGNN: Distribution-aware Fairness in Graph Neural Network","repo_url":"https://github.com/sybeam27/FnRGNN","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}