FairMILE: A Multi-Level Framework for Fair and Scalable Graph Representation Learning

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.