FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization
Fuente:
arXiv
Saved in:
| Main Authors: | Yang, Cheng, Liu, Jixi, Yan, Yunhe, Shi, Chuan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks
by: Zhan, Duna, et al.
Published: (2024)
by: Zhan, Duna, et al.
Published: (2024)
FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently
by: Cong, Zicun, et al.
Published: (2024)
by: Cong, Zicun, et al.
Published: (2024)
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation
by: Li, Chengyu, et al.
Published: (2024)
by: Li, Chengyu, et al.
Published: (2024)
Endowing Pre-trained Graph Models with Provable Fairness
by: Zhang, Zhongjian, et al.
Published: (2024)
by: Zhang, Zhongjian, et al.
Published: (2024)
Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
by: Zhang, He, et al.
Published: (2023)
by: Zhang, He, et al.
Published: (2023)
FairWire: Fair Graph Generation
by: Kose, O. Deniz, et al.
Published: (2024)
by: Kose, O. Deniz, et al.
Published: (2024)
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
by: Fawkes, Jake, et al.
Published: (2024)
by: Fawkes, Jake, et al.
Published: (2024)
Fair Graph Representation Learning via Sensitive Attribute Disentanglement
by: Zhu, Yuchang, et al.
Published: (2024)
by: Zhu, Yuchang, et al.
Published: (2024)
Rethinking Fair Graph Neural Networks from Re-balancing
by: Li, Zhixun, et al.
Published: (2024)
by: Li, Zhixun, et al.
Published: (2024)
GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint
by: Xu, Paiheng, et al.
Published: (2023)
by: Xu, Paiheng, et al.
Published: (2023)
FairGT: A Fairness-aware Graph Transformer
by: Luo, Renqiang, et al.
Published: (2024)
by: Luo, Renqiang, et al.
Published: (2024)
What is Fair? Defining Fairness in Machine Learning for Health
by: Gao, Jianhui, et al.
Published: (2024)
by: Gao, Jianhui, et al.
Published: (2024)
Fairness-Aware Graph Representation Learning with Limited Demographic Information
by: Wang, Zichong, et al.
Published: (2025)
by: Wang, Zichong, et al.
Published: (2025)
Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
by: Duan, Yuying, et al.
Published: (2024)
by: Duan, Yuying, et al.
Published: (2024)
Migrate Demographic Group For Fair GNNs
by: Hu, YanMing, et al.
Published: (2023)
by: Hu, YanMing, et al.
Published: (2023)
Achieving Fairness Across Local and Global Models in Federated Learning
by: Makhija, Disha, et al.
Published: (2024)
by: Makhija, Disha, et al.
Published: (2024)
Rethinking Fair Representation Learning for Performance-Sensitive Tasks
by: Jones, Charles, et al.
Published: (2024)
by: Jones, Charles, et al.
Published: (2024)
A Sequentially Fair Mechanism for Multiple Sensitive Attributes
by: Hu, François, et al.
Published: (2023)
by: Hu, François, et al.
Published: (2023)
FairHome: A Fair Housing and Fair Lending Dataset
by: Bagalkotkar, Anusha, et al.
Published: (2024)
by: Bagalkotkar, Anusha, et al.
Published: (2024)
Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach
by: Shi, Tianhao, et al.
Published: (2024)
by: Shi, Tianhao, et al.
Published: (2024)
Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks
by: He, Erhu, et al.
Published: (2024)
by: He, Erhu, et al.
Published: (2024)
BoostFGL: Boosting Fairness in Federated Graph Learning
by: Chen, Zekai, et al.
Published: (2026)
by: Chen, Zekai, et al.
Published: (2026)
On Demographic Group Fairness Guarantees in Deep Learning
by: Luo, Yan, et al.
Published: (2024)
by: Luo, Yan, et al.
Published: (2024)
Fairness and/or Privacy on Social Graphs
by: Surma, Bartlomiej, et al.
Published: (2025)
by: Surma, Bartlomiej, et al.
Published: (2025)
Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement
by: Chang, Wenjing, et al.
Published: (2024)
by: Chang, Wenjing, et al.
Published: (2024)
FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA
by: Li, Minghan, et al.
Published: (2025)
by: Li, Minghan, et al.
Published: (2025)
Model-Agnostic Fairness Regularization for GNNs with Incomplete Sensitive Information
by: Kejani, Mahdi Tavassoli, et al.
Published: (2025)
by: Kejani, Mahdi Tavassoli, et al.
Published: (2025)
FairMT: Fairness for Heterogeneous Multi-Task Learning
by: Hu, Guanyu, et al.
Published: (2025)
by: Hu, Guanyu, et al.
Published: (2025)
Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
by: Schröder, Maresa, et al.
Published: (2023)
by: Schröder, Maresa, et al.
Published: (2023)
Fairpriori: Improving Biased Subgroup Discovery for Deep Neural Network Fairness
by: Zhou, Kacy, et al.
Published: (2024)
by: Zhou, Kacy, et al.
Published: (2024)
Counterfactual Fairness with Graph Uncertainty
by: Valério, Davi, et al.
Published: (2026)
by: Valério, Davi, et al.
Published: (2026)
Fairness Evaluation with Item Response Theory
by: Xu, Ziqi, et al.
Published: (2024)
by: Xu, Ziqi, et al.
Published: (2024)
Achievable Fairness on Your Data With Utility Guarantees
by: Taufiq, Muhammad Faaiz, et al.
Published: (2024)
by: Taufiq, Muhammad Faaiz, et al.
Published: (2024)
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
by: Small, Edward, et al.
Published: (2022)
by: Small, Edward, et al.
Published: (2022)
Fair Sampling in Diffusion Models through Switching Mechanism
by: Choi, Yujin, et al.
Published: (2024)
by: Choi, Yujin, et al.
Published: (2024)
Learning Fair Representations with Kolmogorov-Arnold Networks
by: Priyadarshini, Amisha, et al.
Published: (2025)
by: Priyadarshini, Amisha, et al.
Published: (2025)
A Fair Loss Function for Network Pruning
by: Meyer, Robbie, et al.
Published: (2022)
by: Meyer, Robbie, et al.
Published: (2022)
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering
by: Yang, Yifan, et al.
Published: (2024)
by: Yang, Yifan, et al.
Published: (2024)
AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
by: Huang, Yinghui, et al.
Published: (2024)
by: Huang, Yinghui, et al.
Published: (2024)
Learning Fair Models without Sensitive Attributes: A Generative Approach
by: Zhu, Huaisheng, et al.
Published: (2022)
by: Zhu, Huaisheng, et al.
Published: (2022)
Similar Items
-
Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks
by: Zhan, Duna, et al.
Published: (2024) -
FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently
by: Cong, Zicun, et al.
Published: (2024) -
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation
by: Li, Chengyu, et al.
Published: (2024) -
Endowing Pre-trained Graph Models with Provable Fairness
by: Zhang, Zhongjian, et al.
Published: (2024) -
Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
by: Zhang, He, et al.
Published: (2023)