FairGSE: Fairness-Aware Graph Neural Network Without High False Positive Rates

Zhenqiang Ye; Jinjie Lu; Tianlong Gu; Fengrui Hao; Xuemin Wang

AAAI 2026technical0 citations

FairGSE: Fairness-Aware Graph Neural Network Without High False Positive Rates

Zhenqiang Ye, Jinjie Lu, Tianlong Gu, Fengrui Hao, Xuemin Wang

Abstract

Graph neural networks (GNNs) have emerged as the mainstream paradigm for graph representation learning due to their effective message aggregation. However, this advantage also amplifies biases inherent in graph topology, raising fairness concerns. Existing fairness-aware GNNs provide satisfactory performance on fairness metrics such as Statistical Parity and Equal Opportunity while maintaining acceptable accuracy trade-offs. Unfortunately, we observe that this pursuit of fairness metrics neglects the GNN

BibTeX

@inproceedings{aaai2026_fairgsefairnessa,
  title = {FairGSE: Fairness-Aware Graph Neural Network Without High False Positive Rates},
  author = {Zhenqiang Ye and Jinjie Lu and Tianlong Gu and Fengrui Hao and Xuemin Wang},
  booktitle = {AAAI 2026},
  year = {2026}
}