Fairness-Aware Graph Representation Learning with Limited Demographic Information
Ensuring fairness in Graph Neural Networks is fundamental to promoting trustworthy and socially responsible machine learning systems. In response, numerous fair...
Abstract
Ensuring fairness in Graph Neural Networks is fundamental to promoting trustworthy and socially responsible machine learning systems. In response, numerous fair graph learning methods have been proposed in recent years. However, most of them assume full access to demographic information, a requirement rarely met in practice due to privacy, legal, or regulatory restrictions. To this end, this paper introduces a novel fair graph learning framework that mitigates bias in graph learning under limited demographic information. Specifically, we propose a mechanism guided by partial demographic data to generate proxies for demographic information and design a strategy that enforces consistent node embeddings across demographic groups. In addition, we develop an adaptive confidence strategy that dynamically adjusts each node's contribution to fairness and utility based on prediction confidence. We further provide theoretical analysis demonstrating that our framework, FairGLite, achieves provable upper bounds on group fairness metrics, offering formal guarantees for bias mitigation. Through extensive experiments on multiple datasets and fair graph learning frameworks, we demonstrate the framework's effectiveness in both mitigating bias and maintaining model utility.
Access Full Paper
This research paper is available on arXiv, an open-access archive for academic preprints.
Citation
Zichong Wang. "Fairness-Aware Graph Representation Learning with Limited Demographic Information." arXiv preprint. 2025-11-17. http://arxiv.org/abs/2511.13540v1
About arXiv
arXiv is a free distribution service and open-access archive for scholarly articles in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering, systems science, and economics.
Disclaimer: This article is for informational purposes only and does not constitute financial advice. Consult a licensed financial advisor before making investment decisions.
Related Articles
- Artificial Intelligence-driven Intelligent Wearable Systems: A full-stack Integration from Material Design to Personalized Interaction
- Automated Construction of Medical Indicator Knowledge Graphs Using Retrieval Augmented Large Language Models
- In-Situ Growth of Halide Perovskite Single Crystals and Thin Films on Optical Fiber End Facets
Disclaimer
This article is for informational purposes only and does not constitute financial, legal, or tax advice. SwissFinanceAI is not a licensed financial services provider. Always consult a qualified professional before making financial decisions.
References
- [1]ResearchCredibility: 9/10Zichong Wang. "Fairness-Aware Graph Representation Learning with Limited Demographic Information." arXiv.org. November 17, 2025. Accessed November 18, 2025.
Transparency Notice: This article may contain AI-assisted content. All citations link to verified sources. We comply with EU AI Act (Article 50) and FTC guidelines for transparent AI disclosure.
Original Source
This article is based on Fairness-Aware Graph Representation Learning with Limited Demographic Information (arXiv.org)
