论文标题
Fairedit：通过贪婪的图编辑在图形神经网络中保留公平性
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing
论文作者
论文摘要
事实证明，图形神经网络（GNN）在基础数据是图形的预测建模任务中表现出色。但是，由于GNN被广泛用于以人为本的应用中，因此出现了公平性的问题。虽然边缘删除是一种用于促进GNN公平性的常见方法，但它何时固有地缺少公平连接时，它却没有考虑。在这项工作中，我们考虑了未开发的边缘添加方法，并伴有删除，以促进公平。我们提出了两种模型不足的算法来执行边缘编辑：蛮力方法和连续的近似方法，fairedit。 Fairedit通过利用公平损失的梯度信息来找到有效的边缘编辑，以找到改善公平性的边缘。我们发现Fairedit优于许多数据集和GNN方法的标准培训，同时对许多最新方法进行了相当的效果，这表明了Fairedit可以改善许多领域和模型的公平性。
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.
