Graph Neural Networks (GNNs) have proven to excel in predictive modeling tasks where the underlying data is a graph. However, as GNNs are extensively used in human-centered applications, the issue of fairness has arisen. While edge deletion is a common method used to promote fairness in GNNs, it fails to consider when data is inherently missing fair connections. In this work we consider the unexplored method of edge addition, accompanied by deletion, to promote fairness. We propose two model-agnostic algorithms to perform edge editing: a brute force approach and a continuous approximation approach, FairEdit. FairEdit performs efficient edge editing by leveraging gradient information of a fairness loss to find edges that improve fairness. We find that FairEdit outperforms standard training for many data sets and GNN methods, while performing comparably to many state-of-the-art methods, demonstrating FairEdit's ability to improve fairness across many domains and models.

本研究探讨如何通过新增边缘来促进基于图神经网络的公平性，并提出了两种无需预训练的算法：暴力方法和连续逼近方法FairEdit。结果表明，FairEdit在多个数据集和GNN方法上表现优异，同时能够提高公平性，在多个领域和模型中有着应用前景。

FairEdit: 通过贪心图编辑保持图神经网络的公平性