2 papers
cs.LG2025
FairACE: Achieving Degree Fairness in Graph Neural Networks via Contrastive and Adversarial Group-Balanced Training
Jiaxin Liu, Xiaoqian Jiang, Xiang Li +2
Fairness has been a significant challenge in graph neural networks (GNNs) since degree biases often result in un-equal prediction performance among nodes with varying degrees. Exis…
cs.LG2024
Heterogeneous Graph Contrastive Learning with Spectral Augmentation
Jing Zhang, Xiaoqian Jiang, Yingjie Xie +1
Heterogeneous graphs can well describe the complex entity relationships in the real world. For example, online shopping networks contain multiple physical types of consumers and pr…