Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks. Despite this success, GNNs suffer from fairness issues that arise as a result of the underlying graph data and the fundamental aggregation mechanism that lies at the heart of the large class of GNN models. In this article, we examine and categorize fairness techniques for improving the fairness of GNNs. Previous work on fair GNN models and techniques are discussed in terms of whether they focus on improving fairness during a preprocessing step, during training, or in a post-processing phase. Furthermore, we discuss how such techniques can be used together whenever appropriate, and highlight the advantages and intuition as well. We also introduce an intuitive taxonomy for fairness evaluation metrics including graph-level fairness, neighborhood-level fairness, embedding-level fairness, and prediction-level fairness metrics. In addition, graph datasets that are useful for benchmarking the fairness of GNN models are summarized succinctly. Finally, we highlight key open problems and challenges that remain to be addressed.

翻译：图神经网络（GNNs）因其在诸多基础学习任务中的强大表征能力与最先进的预测性能而日益重要。尽管取得了成功，但GNNs仍面临公平性问题，这些问题源于底层图数据以及作为大多数GNN模型核心的基础聚合机制。本文考察并分类了用于提升GNNs公平性的技术。以往关于公平GNN模型与技术的研究，依据其聚焦于预处理阶段、训练阶段还是后处理阶段来提升公平性进行讨论。此外，我们还探讨了如何在适当时机将这些技术结合使用，并强调了其优势与直观理解。我们引入了一种直观的公平性评估指标分类法，包括图级公平性、邻域级公平性、嵌入级公平性与预测级公平性指标。同时，简要总结了适用于GNN模型公平性基准测试的图数据集。最后，我们指出了尚待解决的关键开放问题与挑战。