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Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New
  Benchmark

Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark

9 March 2024
Xiaowei Qian
Zhimeng Guo
Jialiang Li
Haitao Mao
Bingheng Li
Suhang Wang
Yao Ma
ArXivPDFHTML

Papers citing "Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark"

4 / 4 papers shown
Title
When Graph Neural Network Meets Causality: Opportunities, Methodologies
  and An Outlook
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
Wenzhao Jiang
Hao Liu
Hui Xiong
CML
AI4CE
19
2
0
19 Dec 2023
Improving Molecular Contrastive Learning via Faulty Negative Mitigation
  and Decomposed Fragment Contrast
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast
Yuyang Wang
Rishikesh Magar
Chen Liang
A. Farimani
30
78
0
18 Feb 2022
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
283
4,143
0
23 Aug 2019
Learning Adversarially Fair and Transferable Representations
Learning Adversarially Fair and Transferable Representations
David Madras
Elliot Creager
T. Pitassi
R. Zemel
FaML
205
663
0
17 Feb 2018
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