Fairness-Aware Graph Neural Networks: A Survey

TL;DR

This survey reviews fairness issues in Graph Neural Networks, categorizing techniques, evaluation metrics, datasets, and highlighting open challenges to guide future research in fair GNN development.

Contribution

It provides a comprehensive taxonomy of fairness techniques, metrics, and datasets for GNNs, and discusses how to combine different fairness approaches.

Findings

Categorized fairness techniques into preprocessing, training, and post-processing methods.

Introduced a taxonomy of fairness evaluation metrics at multiple levels.

Summarized datasets useful for benchmarking fairness in GNNs.

Abstract

Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks. Despite this success, GNNs suffer from fairness issues that arise as a result of the underlying graph data and the fundamental aggregation mechanism that lies at the heart of the large class of GNN models. In this article, we examine and categorize fairness techniques for improving the fairness of GNNs. Previous work on fair GNN models and techniques are discussed in terms of whether they focus on improving fairness during a preprocessing step, during training, or in a post-processing phase. Furthermore, we discuss how such techniques can be used together whenever appropriate, and highlight the advantages and intuition as well. We also introduce an intuitive taxonomy for fairness evaluation metrics including graph-level fairness, neighborhood-level fairness, embedding-level fairness, and prediction-level fairness metrics. In addition, graph datasets that are useful for benchmarking the fairness of GNN models are summarized succinctly. Finally, we highlight key open problems and challenges that remain to be addressed.