广西师范大学学报（自然科学版）2026，Vol.44Issue(4)：96-106,11.DOI:10.16088/j.issn.1001-6600.2025122801
基于视图解耦与反事实增强的公平图学习
Fair graph learning via view disentanglement and counterfactual augmentation
摘要
Abstract
Graph Neural Network(GNN)have been widely applied in numerous real-world scenarios due to their powerful modeling capabilities for graph-structured data.Recent studies indicate that GNN may inherit and amplify biases inherent in training data,leading to unfair treatment of specific groups defined by sensitive attributes.To mitigate bias in graph data,this paper proposes FairDC,a fair graph learning framework based on view decoupling and counterfactual augmentation,which addresses feature bias and structural bias separately.The framework first decouples raw graph data into feature views and structural views,then introduces counterfactual views.Finally,a multi-view fusion strategy is employed to learn fair node representations.Experimental results on multiple benchmark datasets demonstrate that FairDC maintains stable prediction performance while reducing fairness metrics DP and EO by 32％and 36％,respectively,compared with the state-of-the-art baseline DAB.This validates the proposed method's effective trade-off between utility and fairness.关键词
图神经网络/公平性/解纠缠/反事实增强/多视图融合Key words
graph neural network/fairness/disentanglement/counterfactual augmentation/multi-view fusion分类
信息技术与安全科学引用本文复制引用
韦吴杰,陈庆锋..基于视图解耦与反事实增强的公平图学习[J].广西师范大学学报（自然科学版）,2026,44(4):96-106,11.基金项目
广西科技基地和人才专项(桂科 AD24010011) （桂科 AD24010011）
广西重点研发计划(桂科 AB25069095) （桂科 AB25069095）
