  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.
