Huafei Huang — accepted AI-conference papers

2025

FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning

Renqiang Luo, Huafei Huang, Ivan Lee, Chengpei Xu, Jianzhong Qi, Feng Xia

AAAI 2025technical

Recent studies have highlighted significant fairness issues in Graph Transformer (GT) models, particularly against subgroups defined by sensitive features. Additionally, GTs are computationally intensive and memory-demanding, limiting their application to large-scale graphs. Our experiments demonstr…

Cited by 1 Source ↗PDF ↗code ↗Scholar ↗

2024

FairGT: A Fairness-aware Graph Transformer

Renqiang Luo, Huafei Huang, Shuo Yu, Xiuzhen Zhang, Feng Xia

IJCAI 2024poster

The design of Graph Transformers (GTs) often neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups. Since GTs encode graph information without relying on message-passing mechanisms, conventional fairness-aware graph learning methods are not directly a…

Cited by 11 Source ↗PDF ↗code ↗Scholar ↗