  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.
