With the widespread use of Graph Neural Networks (GNNs) for representation learning from network data, the fairness of GNN models has raised great attention lately. Fair GNNs aim to ensure that node representations can be accurately classified, but not easily associated with a specific group. Existing advanced approaches essentially enhance the generalisation of node representation in combination with data augmentation strategy, and do not directly impose constraints on the fairness of GNNs. In this work, we identify that a fundamental reason for the unfairness of GNNs in social network learning is the phenomenon of social homophily, i.e., users in the same group are more inclined to congregate. The message-passing mechanism of GNNs can cause users in the same group to have similar representations due to social homophily, leading model predictions to establish spurious correlations with sensitive attributes. Inspired by this reason, we propose a method called Equity-Aware GNN (EAGNN) towards fair graph representation learning. Specifically, to ensure that model predictions are independent of sensitive attributes while maintaining prediction performance, we introduce constraints for fair representation learning based on three principles: sufficiency, independence, and separation. We theoretically demonstrate that our EAGNN method can effectively achieve group fairness. Extensive experiments on three datasets with varying levels of social homophily illustrate that our EAGNN method achieves the state-of-the-art performance across two fairness metrics and offers competitive effectiveness.

翻译：随着图神经网络（GNNs）在网络数据表示学习中的广泛应用，GNN模型的公平性问题近来引起了极大关注。公平的GNN旨在确保节点表示能够被准确分类，但不易与特定群体相关联。现有的先进方法本质上是通过结合数据增强策略来提升节点表示的泛化能力，并未直接对GNN的公平性施加约束。在本研究中，我们发现GNN在社交网络学习中不公平的一个根本原因是社会同质性现象，即同一群体中的用户更倾向于聚集。由于社会同质性，GNN的消息传递机制会导致同一群体中的用户具有相似的表示，从而使模型预测与敏感属性建立虚假关联。受此启发，我们提出了一种名为公平感知图神经网络（EAGNN）的方法，以实现公平的图表示学习。具体而言，为了在保持预测性能的同时确保模型预测独立于敏感属性，我们基于充分性、独立性和分离性三个原则引入了公平表示学习的约束。我们从理论上证明了我们的EAGNN方法能够有效实现群体公平性。在三个具有不同社会同质性水平的数据集上进行的大量实验表明，我们的EAGNN方法在两个公平性指标上均达到了最先进的性能，并提供了具有竞争力的有效性。