Fairness in Graph Learning Augmented with Machine Learning: A Survey
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Luo, Renqiang Xu, Ziqi Zhang, Xikun Qing, Qing Huang, Huafei Dai, Enyan Wang, Zhe Yang, Bo |
| author_facet | Luo, Renqiang Xu, Ziqi Zhang, Xikun Qing, Qing Huang, Huafei Dai, Enyan Wang, Zhe Yang, Bo |
| contents | Augmenting specialised machine learning techniques into traditional graph learning models has achieved notable success across various domains, including federated graph learning, dynamic graph learning, and graph transformers. However, the intricate mechanisms of these specialised techniques introduce significant challenges in maintaining model fairness, potentially resulting in discriminatory outcomes in high-stakes applications such as recommendation systems, disaster response, criminal justice, and loan approval. This paper systematically examines the unique fairness challenges posed by Graph Learning augmented with Machine Learning (GL-ML). It highlights the complex interplay between graph learning mechanisms and machine learning techniques, emphasising how the augmentation of machine learning both enhances and complicates fairness. Additionally, we explore four critical techniques frequently employed to improve fairness in GL-ML methods. By thoroughly investigating the root causes and broader implications of fairness challenges in this rapidly evolving field, this work establishes a robust foundation for future research and innovation in GL-ML fairness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21296 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fairness in Graph Learning Augmented with Machine Learning: A Survey Luo, Renqiang Xu, Ziqi Zhang, Xikun Qing, Qing Huang, Huafei Dai, Enyan Wang, Zhe Yang, Bo Machine Learning Artificial Intelligence Augmenting specialised machine learning techniques into traditional graph learning models has achieved notable success across various domains, including federated graph learning, dynamic graph learning, and graph transformers. However, the intricate mechanisms of these specialised techniques introduce significant challenges in maintaining model fairness, potentially resulting in discriminatory outcomes in high-stakes applications such as recommendation systems, disaster response, criminal justice, and loan approval. This paper systematically examines the unique fairness challenges posed by Graph Learning augmented with Machine Learning (GL-ML). It highlights the complex interplay between graph learning mechanisms and machine learning techniques, emphasising how the augmentation of machine learning both enhances and complicates fairness. Additionally, we explore four critical techniques frequently employed to improve fairness in GL-ML methods. By thoroughly investigating the root causes and broader implications of fairness challenges in this rapidly evolving field, this work establishes a robust foundation for future research and innovation in GL-ML fairness. |
| title | Fairness in Graph Learning Augmented with Machine Learning: A Survey |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.21296 |