Graph Neural networks (GNNs) have been applied in many scenarios due to the superior performance of graph learning. However, fairness is always ignored when designing GNNs. As a consequence, biased information in training data can easily affect vanilla GNNs, causing biased results toward particular demographic groups (divided by sensitive attributes, such as race and age). There have been efforts to address the fairness issue. However, existing fair techniques generally divide the demographic groups by raw sensitive attributes and assume that are fixed. The biased information correlated with raw sensitive attributes will run through the training process regardless of the implemented fair techniques. It is urgent to resolve this problem for training fair GNNs. To tackle this problem, we propose a brand new framework, FairMigration, which can dynamically migrate the demographic groups instead of keeping that fixed with raw sensitive attributes. FairMigration is composed of two training stages. In the first stage, the GNNs are initially optimized by personalized self-supervised learning, and the demographic groups are adjusted dynamically. In the second stage, the new demographic groups are frozen and supervised learning is carried out under the constraints of new demographic groups and adversarial training. Extensive experiments reveal that FairMigration balances model performance and fairness well.

翻译：图神经网络（GNN）因其出色的图学习性能而被广泛应用于众多场景。然而，在设计GNN时公平性常被忽视。因此，训练数据中的偏差信息容易影响普通GNN，导致对特定人口群体（按种族、年龄等敏感属性划分）产生偏差结果。已有研究致力于解决公平性问题，但现有公平技术通常按原始敏感属性划分人口群体并假定其固定不变。无论采用何种公平技术，与原始敏感属性相关的偏差信息都会贯穿整个训练过程。训练公平GNN亟需解决这一问题。为此，我们提出全新框架FairMigration，该框架能够动态迁移人口群体，而非保持原始敏感属性的固定划分。FairMigration包含两个训练阶段：第一阶段通过个性化自监督学习对GNN进行初始优化，并动态调整人口群体划分；第二阶段冻结新的人口群体划分，在该划分约束下结合对抗训练进行监督学习。大量实验表明，FairMigration能够很好地平衡模型性能与公平性。