8 citations · 13 across the 8 of their papers we have counts for
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cs.LG2024★ 1 cited
Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks
Duna Zhan, Dongliang Guo, Pengsheng Ji +1
Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and learning from complex data structured as graphs, demonstrating remarkable effectiveness in various ap…
cs.LG2023★ 2 cited
Trustworthy Representation Learning Across Domains
Ronghang Zhu, Dongliang Guo, Daiqing Qi +3
As AI systems have obtained significant performance to be deployed widely in our daily live and human society, people both enjoy the benefits brought by these technologies and suff…
cs.LG2023★ 2 cited
Fair Attribute Completion on Graph with Missing Attributes
Dongliang Guo, Zhixuan Chu, Sheng Li
Tackling unfairness in graph learning models is a challenging task, as the unfairness issues on graphs involve both attributes and topological structures. Existing work on fair gra…