揭示社交图结构偏差对链接预测公平性的影响，挑战仅靠同质性解释公平性问题。
Structural Bias Beyond Homophily: A Study of Fairness in Link Prediction
- 构建拓扑偏差分类体系与可控合成图生成方法
- 发现公平性结果与图拓扑强相关，现有方法仍受非同质性偏差影响
- 适合关注图学习公平性评估的科研人员和应用开发者
图链接预测（LP）在求职推荐、好友匹配等社会影响重大场景中至关重要，公平性成为关键关切。尽管许多公平性方法通过操作图结构来缓解预测差异，但社交图固有的拓扑偏差长期未被充分理解，且常被简单归因于同质性。本文研究了拓扑偏差与链接预测公平性之间的关系，提出拓扑偏差度量的分类体系，并引入一种可生成具有控制结构特性的多样化合成图的图生成方法。利用该合成图数据集，我们实证发现公平性结果与图拓扑密切相关，且当前公平性方法仍对同质性以外的结构性偏差敏感。这些发现凸显了在公平图学习中开展基于结构基础的评估的必要性。
原文摘要 · Abstract (English)
Graph link prediction (LP) plays a critical role in socially impactful applications such as job recommendation and friendship formation, making fairness a critical concern in this task. While many fairness-aware methods manipulate graph structures to mitigate prediction disparities, the topological biases inherent to social graphs remain poorly understood and are consistently conflated with homophily alone. In this work, we study the relationship between structural biases and fairness outcomes in LP. To this end, we formalize a taxonomy of topological bias measures and introduce a graph generation method producing a diverse corpus of synthetic graphs with controlled structural properties. Using this corpus, we show empirically that fairness outcomes are strongly correlated with graph topology, and that current fairness-aware methods remain sensitive to structural biases beyond homophily. These findings highlight the need for structurally grounded evaluations in fair graph learning.
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