Graph representation learning models have been deployed for making decisions in multiple high-stakes scenarios. It is therefore critical to ensure that these models are fair. Prior research has shown that graph neural networks can inherit and reinforce the bias present in graph data. Researchers have begun to examine ways to mitigate the bias in such models. However, existing efforts are restricted by their inefficiency, limited applicability, and the constraints they place on sensitive attributes. To address these issues, we present FairMILE a general framework for fair and scalable graph representation learning. FairMILE is a multi-level framework that allows contemporary unsupervised graph embedding methods to scale to large graphs in an agnostic manner. FairMILE learns both fair and high-quality node embeddings where the fairness constraints are incorporated in each phase of the framework. Our experiments across two distinct tasks demonstrate that FairMILE can learn node representations that often achieve superior fairness scores and high downstream performance while significantly outperforming all the baselines in terms of efficiency.

翻译：图表示学习模型已被部署用于多个高风险场景的决策制定。因此，确保这些模型的公平性至关重要。已有研究表明，图神经网络可能继承并强化图数据中存在的偏差。研究者已开始探索缓解此类模型偏差的方法。然而，现有工作受限于效率低下、适用性有限以及对敏感属性的约束等问题。为解决上述问题，我们提出FairMILE——一个面向公平与可扩展图表示学习的通用框架。FairMILE采用多层级架构，能以不可知方式使当代无监督图嵌入方法扩展至大规模图。该框架在每一阶段均嵌入公平性约束，从而学习兼具公平性与高质量特性的节点嵌入。我们在两项不同任务上的实验表明，FairMILE学习的节点表示在公平性评分与下游任务性能上往往表现优异，同时其效率显著优于所有基线方法。