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Xuemin Wang

2 accepted papers

2026

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

AAAI 2026technical

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 pe

Cited by 0SourcePDFScholar
2025

BIGFR: Bridging Individual and Group Fairness in Recommendation Systems

ICASSP 2025accepted

Recommendation systems enhance user experience and retention by offering personalized content. As they increasingly influence social resource allocation (e.g., job recommendations), ensuring fair recommendations is becoming essential. Fairness notions in recommendation systems are mainly divided int…

Cited by 0SourceScholar