  Graph neural networks (GNNs) have been extensively developed for graph
representation learning in various application domains. However, similar to all
other neural networks models, GNNs suffer from the black-box problem as people
cannot understand the mechanism underlying them. To solve this problem, several
GNN explainability methods have been proposed to explain the decisions made by
GNNs. In this survey, we give an overview of the state-of-the-art GNN
explainability methods and how they are evaluated. Furthermore, we propose a
new evaluation metric and conduct thorough experiments to compare GNN
explainability methods on real world datasets. We also suggest future
directions for GNN explainability.
