论文标题
Fairmile：建立一个有效的公平图表学习框架
FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning
论文作者
论文摘要
图表学习模型在许多现实世界应用中都表现出很大的能力。然而，先前的研究表明，这些模型可以学习有偏见的表示，从而导致歧视性结果。已经提出了一些工作来减轻图表表示的偏差。但是，大多数现有的作品都需要出色的时间和计算资源来进行培训和微调。为此，我们研究了有效的公平图表学习的问题，并提出了一个新颖的框架Fairmile。 Fairmile是一种多级范式，可以在执行公平和保存实用程序的同时有效地学习图表表示。它可以与任何无监督的嵌入方法结合使用，并适应各种公平的约束。跨不同下游任务的广泛实验表明，在运行时间方面，Fairmile在运行时间方面的表现显着超过了最先进的基线，同时实现了公平和公用事业之间的较高权衡。
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.
