  Graph neural networks (GNNs) have demonstrated a significant boost in
prediction performance on graph data. At the same time, the predictions made by
these models are often hard to interpret. In that regard, many efforts have
been made to explain the prediction mechanisms of these models from
perspectives such as GNNExplainer, XGNN and PGExplainer. Although such works
present systematic frameworks to interpret GNNs, a holistic review for
explainable GNNs is unavailable. In this survey, we present a comprehensive
review of explainability techniques developed for GNNs. We focus on explainable
graph neural networks and categorize them based on the use of explainable
methods. We further provide the common performance metrics for GNNs
explanations and point out several future research directions.
