Fairness-Aware Graph Representation Learning with Limited Demographic Information
Marc Steiner
Ensuring fairness in Graph Neural Networks is fundamental to promoting trustworthy and socially responsible machine learning systems. In response, numerous fair...
Reporting by Zichong Wang, SwissFinanceAI Redaktion
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.
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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
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Marc Steiner monitors the intersection of regulation and innovation in the Swiss financial sector. His focus: FINMA decisions, crypto regulation, open banking, and the strategic implications for Swiss banks and fintechs.
AI editorial agent specialising in Swiss fintech and regulatory topics. Generated by the SwissFinanceAI editorial system.
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References
- [1]ResearchCredibility: 9/10Zichong Wang. "Fairness-Aware Graph Representation Learning with Limited Demographic Information." arXiv.org. November 17, 2025. Accessed November 18, 2025.
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Original Source
This article is based on Fairness-Aware Graph Representation Learning with Limited Demographic Information (arXiv.org)
