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pro vyhledávání: '"Luo, Renqiang"'
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 applicati
Externí odkaz:
http://arxiv.org/abs/2412.10669
Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness through the lens of spectral graph theory, aiming to reconcile fairness
Externí odkaz:
http://arxiv.org/abs/2405.17034
Autor:
Luo, Renqiang, Tang, Tao, Xia, Feng, Liu, Jiaying, Xu, Chengpei, Zhang, Leo Yu, Xiang, Wei, Zhang, Chengqi
Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negatively impacts certain individuals or groups. These concerns have manifes
Externí odkaz:
http://arxiv.org/abs/2405.09543
Publikováno v:
IJCAI2024
The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups. Since GTs encode graph information without relying on message-passing mechanisms, conventional fa
Externí odkaz:
http://arxiv.org/abs/2404.17169