Efficient Fair Graph Representation Learning Using a Multi-level Framework

Graph representation learning models have demonstrated great capability in many real-world applications. Nevertheless, prior research reveals that these models can learn biased representations leading to unfair 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. In this demonstration, we propose a framework FairMILE for efficient fair graph representation learning. FairMILE allows the user to efficiently learn fair graph representations while preserving utility. In addition, FairMILE can work in conjunction with any unsupervised embedding approach based on the user's preference and accommodate various fairness constraints. The demonstration will introduce the methodology of FairMILE, showcase how to set up and run this framework, and demonstrate our effectiveness and efficiency to the audience through both quantitative metrics and visualization.