梳理增强图学习中的公平性挑战，提出新框架FairGX
Fairness in Augmented Graph Learning: A Survey
- 提出FairGX框架，识别联邦聚合双侧差异等新型偏见源
- 构建分类体系，归纳现有方法的技术路径与公平目标
- 适合关注图学习公平性、隐私协同的科研人员参考
图学习已发展为融合特定机器学习技术的增强图学习（AGL），如联邦学习、图变压器和图压缩。尽管提升了模型效能，但AGL引入了传统图神经网络去偏框架无法覆盖的独特交叉公平性挑战。本文系统研究这一新兴领域，提出FairGX范式，揭示了联邦聚合中双侧差异、注意力头偏差等新型偏见来源。建立结构化分类体系，按技术整合方式与公平目标对现有文献进行归类。分析不同机器学习范式对算法公平性的影响，强调人本应用中的特殊挑战及统一框架的缺失。最后提出五个关键未来方向：AGL专用公平度量、公平-隐私协同机制，以及面向图的LLM/基于图的LLM的公平性设计。本综述为复杂机器学习环境中构建稳健且公平的图系统提供基础路线图。
原文摘要 · Abstract (English)
Graph learning has evolved into Augmented Graph Learning (AGL) by integrating specialized machine learning (ML) techniques. Examples include federated learning, graph transformers, and graph condensation. While enhancing model utility, AGL introduces unique intersectional fairness challenges that traditional GNN debiasing frameworks, which primarily focus on message-passing regulations, fail to address. This paper provides a systematic investigation into this emerging field, termed FairGX. We first delineate the shift from conventional fairness-aware graph learning to the FairGX paradigm, identifying novel bias sources inherent in ML augmentations, such as dual-side disparities in federated aggregation and attention-head skewness. A structured taxonomy is established to categorize existing literature based on their technical integration and fairness objectives. Furthermore, we analyze the impact of diverse ML paradigms on algorithmic equity, emphasizing the unique challenges in human-centered applications and the absence of a unified framework. We conclude by identifying five critical future directions, including novel metrics for AGL, fairness-privacy synergy, and Fairness-aware LLM4Graph/Graph4LLM. This survey serves as a foundational roadmap for developing robust and equitable graph systems in complex ML environments.
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