The rise of graph representation learning as the primary solution for many different network science tasks led to a surge of interest in the fairness of this family of methods. Link prediction, in particular, has a substantial social impact. However, link prediction algorithms tend to increase the segregation in social networks by disfavoring the links between individuals in specific demographic groups. This paper proposes a novel way to enforce fairness on graph neural networks with a fine-tuning strategy. We Drop the unfair Edges and, simultaneously, we Adapt the model's parameters to those modifications, DEA in short. We introduce two covariance-based constraints designed explicitly for the link prediction task. We use these constraints to guide the optimization process responsible for learning the new "fair" adjacency matrix. One novelty of DEA is that we can use a discrete yet learnable adjacency matrix in our fine-tuning. We demonstrate the effectiveness of our approach on five real-world datasets and show that we can improve both the accuracy and the fairness of the link prediction tasks. In addition, we present an in-depth ablation study demonstrating that our training algorithm for the adjacency matrix can be used to improve link prediction performances during training. Finally, we compute the relevance of each component of our framework to show that the combination of both the constraints and the training of the adjacency matrix leads to optimal performances.

翻译：图表示学习作为解决众多网络科学任务的主流方法，其兴起引发了学界对该类方法公平性问题的广泛关注。特别地，链接预测具有显著的社会影响。然而，链接预测算法往往通过削弱特定人口群体间个体连接来加剧社交网络中的隔离现象。本文提出了一种新颖的图神经网络公平性增强方法，采用微调策略实现：一方面丢弃不公平均衡的边（即DEA中的Drop），另一方面同步调整模型参数以适应这些变化。我们为链接预测任务专门设计了两种基于协方差的约束条件，并将其用于指导新型"公平"邻接矩阵的学习优化过程。DEA的创新之处在于，我们在微调过程中能够使用离散化但可训练的邻接矩阵。通过在五个真实世界数据集上的实验，我们验证了该方法既能提升链接预测精度，又能增强公平性。此外，深入的消融研究表明，我们提出的邻接矩阵训练算法可在模型训练阶段有效提升链接预测性能。最后，通过计算框架各组成部分的相关重要性，我们证明了约束条件与邻接矩阵训练的协同作用是实现最优性能的关键。