Graph representation learning models have demonstrated great capability in many real-world applications. Nevertheless, prior research indicates that these models can learn biased representations leading to discriminatory outcomes. A few works have been proposed to mitigate the bias in graph representations. However, most existing works require exceptional time and computing resources for training and fine-tuning. To this end, we study the problem of efficient fair graph representation learning and propose a novel framework FairMILE. FairMILE is a multi-level paradigm that can efficiently learn graph representations while enforcing fairness and preserving utility. It can work in conjunction with any unsupervised embedding approach and accommodate various fairness constraints. Extensive experiments across different downstream tasks demonstrate that FairMILE significantly outperforms state-of-the-art baselines in terms of running time while achieving a superior trade-off between fairness and utility.

翻译：图表示学习模型已在众多实际应用中展现出卓越能力。然而，先前研究表明，这类模型可能学习到有偏表示，从而导致歧视性结果。尽管已有少量工作致力于缓解图表示中的偏差，但现有方法大多需要异常的时间和计算资源进行训练与微调。为此，我们研究了高效公平图表示学习的问题，并提出了一种新型框架FairMILE。FairMILE采用多层次范式，能够在保持公平性并保留效用的同时高效学习图表示。该框架可与任何无监督嵌入方法协同工作，并适应多种公平性约束。跨不同下游任务的大量实验表明，FairMILE在运行时间上显著优于最先进的基线方法，同时在公平性与效用之间实现了更优的权衡。