The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation

29 November 2023

Papers citing "The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation"

9 / 9 papers shown

Title
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach Xunkai Li Zhengyu Wu Kaichi Yu Hongchao Qin Guang Zeng Rong-Hua Li Guoren Wang 27 0 0 02 May 2025
Fairness in Graph Learning Augmented with Machine Learning: A Survey Renqiang Luo Ziqi Xu X. Zhang Qing Qing Huafei Huang Enyan Dai Z. Wang Bo Yang FaML 52 0 0 30 Apr 2025
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation Chengyu Li Debo Cheng Guixian Zhang Yi Li Shichao Zhang 73 0 0 30 Nov 2024
Fair4Free: Generating High-fidelity Fair Synthetic Samples using Data Free Distillation Md Fahim Sikder Daniel de Leng Fredrik Heintz 19 1 0 02 Oct 2024
Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation Md Fahim Sikder R. Ramachandranpillai Daniel de Leng Fredrik Heintz 22 1 0 20 Aug 2024
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes Yuchang Zhu Jintang Li Yatao Bian Zibin Zheng Liang Chen 23 2 0 19 Jun 2024
Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections Zihan Luo Hong Huang Yongkang Zhou Jiping Zhang Nuo Chen 22 1 0 05 Jun 2024
Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement Wenjing Chang Kay Liu Philip S. Yu Jianjun Yu 51 2 0 03 Jun 2024
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