Conventional graph neural networks (GNNs) are often confronted with fairness issues that may stem from their input, including node attributes and neighbors surrounding a node. While several recent approaches have been proposed to eliminate the bias rooted in sensitive attributes, they ignore the other key input of GNNs, namely the neighbors of a node, which can introduce bias since GNNs hinge on neighborhood structures to generate node representations. In particular, the varying neighborhood structures across nodes, manifesting themselves in drastically different node degrees, give rise to the diverse behaviors of nodes and biased outcomes. In this paper, we first define and generalize the degree bias using a generalized definition of node degree as a manifestation and quantification of different multi-hop structures around different nodes. To address the bias in the context of node classification, we propose a novel GNN framework called Generalized Degree Fairness-centric Graph Neural Network (Deg-FairGNN). Specifically, in each GNN layer, we employ a learnable debiasing function to generate debiasing contexts, which modulate the layer-wise neighborhood aggregation to eliminate the degree bias originating from the diverse degrees among nodes. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our model on both accuracy and fairness metrics.

翻译：传统的图神经网络（GNN）常面临公平性问题，该问题可能源于其输入，包括节点属性和节点周围的邻居。尽管近期提出了若干方法以消除基于敏感属性的偏差，但这些方法忽略了GNN的另一关键输入——节点的邻居，由于GNN依赖邻域结构生成节点表示，邻居可能引入偏差。特别地，节点间不同的邻域结构（表现为节点度的显著差异）会导致节点行为多样性和有偏结果。本文首先利用节点度的广义定义作为不同节点周围多跳结构的表征与量化方式，定义并泛化了度偏差。为应对节点分类中的偏差问题，我们提出了一种新颖的GNN框架——广义度公平图神经网络（Deg-FairGNN）。具体而言，在每个GNN层中，我们采用可学习的去偏函数生成去偏上下文，以调节逐层邻域聚合过程，消除因节点度差异导致的度偏差。在三个基准数据集上的大量实验表明，我们的模型在准确率和公平性指标上均具有有效性。