Tackling unfairness in graph learning models is a challenging task, as the unfairness issues on graphs involve both attributes and topological structures. Existing work on fair graph learning simply assumes that attributes of all nodes are available for model training and then makes fair predictions. In practice, however, the attributes of some nodes might not be accessible due to missing data or privacy concerns, which makes fair graph learning even more challenging. In this paper, we propose FairAC, a fair attribute completion method, to complement missing information and learn fair node embeddings for graphs with missing attributes. FairAC adopts an attention mechanism to deal with the attribute missing problem and meanwhile, it mitigates two types of unfairness, i.e., feature unfairness from attributes and topological unfairness due to attribute completion. FairAC can work on various types of homogeneous graphs and generate fair embeddings for them and thus can be applied to most downstream tasks to improve their fairness performance. To our best knowledge, FairAC is the first method that jointly addresses the graph attribution completion and graph unfairness problems. Experimental results on benchmark datasets show that our method achieves better fairness performance with less sacrifice in accuracy, compared with the state-of-the-art methods of fair graph learning. Code is available at: https://github.com/donglgcn/FairAC.

翻译：解决图学习模型中的不公平问题是一项具有挑战性的任务，因为图上的不公平性既涉及属性也涉及拓扑结构。现有的公平图学习工作通常假设所有节点的属性均可用于模型训练，并据此做出公平预测。然而在实际应用中，由于数据缺失或隐私问题，部分节点的属性可能无法获取，这使得公平图学习更具挑战性。本文提出FairAC（公平属性补全方法），用于补全缺失信息并学习缺失属性图的公平节点嵌入。FairAC采用注意力机制处理属性缺失问题，同时缓解两类不公平性：即来自属性的特征不公平性和因属性补全导致的拓扑不公平性。FairAC可适用于多种同构图，为其生成公平嵌入，从而能够应用于大多数下游任务以提升其公平性表现。据我们所知，FairAC是首个联合解决图属性补全与图不公平性问题的方法。在基准数据集上的实验结果表明，与最先进的公平图学习方法相比，我们的方法能在更少牺牲准确率的情况下实现更优的公平性性能。代码开源地址：https://github.com/donglgcn/FairAC。