Link prediction is a crucial task in network analysis, but it has been shown to be prone to biased predictions, particularly when links are unfairly predicted between nodes from different sensitive groups. In this paper, we study the fair link prediction problem, which aims to ensure that the predicted link probability is independent of the sensitive attributes of the connected nodes. Existing methods typically incorporate debiasing techniques within graph embeddings to mitigate this issue. However, training on large real-world graphs is already challenging, and adding fairness constraints can further complicate the process. To overcome this challenge, we propose FairLink, a method that learns a fairness-enhanced graph to bypass the need for debiasing during the link predictor's training. FairLink maintains link prediction accuracy by ensuring that the enhanced graph follows a training trajectory similar to that of the original input graph. Meanwhile, it enhances fairness by minimizing the absolute difference in link probabilities between node pairs within the same sensitive group and those between node pairs from different sensitive groups. Our extensive experiments on multiple large-scale graphs demonstrate that FairLink not only promotes fairness but also often achieves link prediction accuracy comparable to baseline methods. Most importantly, the enhanced graph exhibits strong generalizability across different GNN architectures.

翻译：链路预测是网络分析中的关键任务，但已被证明容易产生有偏预测，特别是当不同敏感群体节点间的连接被不公平预测时。本文研究公平链路预测问题，其目标是确保预测的连接概率与相连节点的敏感属性无关。现有方法通常在图嵌入中引入去偏技术以缓解该问题。然而，在大型真实世界图上进行训练已颇具挑战性，添加公平性约束会进一步增加训练复杂度。为克服这一挑战，我们提出FairLink方法，该方法通过学习公平性增强图来避免在链路预测器训练过程中进行去偏处理。FairLink通过确保增强图遵循与原始输入图相似的训练轨迹来保持链路预测准确性。同时，该方法通过最小化同一敏感群体节点对与不同敏感群体节点对之间连接概率的绝对差异来增强公平性。我们在多个大规模图数据上的大量实验表明，FairLink不仅能提升公平性，其链路预测精度也常与基线方法相当。最重要的是，增强后的图在不同GNN架构间展现出强大的泛化能力。