Fairness has been taken as a critical metric on machine learning models. Many works studying how to obtain fairness for different tasks emerge. This paper considers obtaining fairness for link prediction tasks, which can be measured by dyadic fairness. We aim to propose a pre-processing methodology to obtain dyadic fairness through data repairing and optimal transport. To obtain dyadic fairness with satisfying flexibility and unambiguity requirements, we transform the dyadic repairing to the conditional distribution alignment problem based on optimal transport and obtain theoretical results on the connection between the proposed alignment and dyadic fairness. The optimal transport-based dyadic fairness algorithm is proposed for graph link prediction. Our proposed algorithm shows superior results on obtaining fairness compared with the other pre-processing methods on two benchmark graph datasets.

翻译：公平被视作机器学习模式的关键衡量标准。 许多研究如何为不同任务取得公平性的工作正在出现。 本文审议了如何为连接预测任务取得公平性的问题, 可以通过dyadic 公平性加以衡量。 我们的目标是提出一种通过数据修复和最佳运输获得dyadic公平性的预处理方法。 为了在满足灵活性和不矛盾的要求的情况下获得dyadic公平性, 我们将双轨修复转化为基于最佳运输的有条件分配协调问题, 并获得关于拟议对齐和dyadic 公平性之间关联的理论结果。 最佳的基于运输的dyadic公平性算法是用来预测图形链接的。 我们提议的算法显示与两个基准图表数据集中的其他预处理方法相比,在获得公平性方面的优异结果。