Graph link prediction (LP) plays a critical role in socially impactful applications, such as job recommendation and friendship formation. Ensuring fairness in this task is thus essential. While many fairness-aware methods manipulate graph structures to mitigate prediction disparities, the topological biases inherent to social graph structures remain poorly understood and are often reduced to homophily alone. This undermines the generalization potential of fairness interventions and limits their applicability across diverse network topologies. In this work, we propose a novel benchmarking framework for fair LP, centered on the structural biases of the underlying graphs. We begin by reviewing and formalizing a broad taxonomy of topological bias measures relevant to fairness in graphs. In parallel, we introduce a flexible graph generation method that simultaneously ensures fidelity to real-world graph patterns and enables controlled variation across a wide spectrum of structural biases. We apply this framework to evaluate both classical and fairness-aware LP models across multiple use cases. Our results provide a fine-grained empirical analysis of the interactions between predictive fairness and structural biases. This new perspective reveals the sensitivity of fairness interventions to beyond-homophily biases and underscores the need for structurally grounded fairness evaluations in graph learning.

翻译：图链路预测在具有社会影响力的应用中扮演着关键角色，例如职位推荐和好友关系建立。因此，确保该任务的公平性至关重要。尽管许多公平感知方法通过操纵图结构来缓解预测差异，但社交图结构固有的拓扑偏差仍鲜为人知，且常被简化为同质性。这削弱了公平干预措施的泛化潜力，并限制了其在多样化网络拓扑中的适用性。在本研究中，我们提出了一种新颖的公平链路预测基准框架，其核心在于底层图的结构偏差。我们首先回顾并形式化了一套与图公平性相关的广泛拓扑偏差度量分类体系。同时，我们引入了一种灵活的图生成方法，该方法在确保对真实世界图模式保真度的同时，能够在一系列广泛的结构偏差范围内实现可控变化。我们应用此框架在多个用例中评估经典及公平感知的链路预测模型。我们的结果提供了预测公平性与结构偏差之间相互作用的细粒度实证分析。这一新视角揭示了公平干预措施对超越同质性的偏差的敏感性，并强调了在图学习中开展基于结构的公平性评估的必要性。