1 Introduction  
1.1 Graph Neural Networks  
1.2 Explainability in ML  
2 Overview  
3 Factual  
3.1 Post-hoc  
3.2 Self-interpretable  
4 Counterfactual Explanation  
4.1 Perturbation-based methods  
4.2 Neural framework-based methods  
4.3 Search-based methods  
5 Others  
5.1 Explainers for Temporal GNNs  
5.2 Global Explainers  
5.3 Causality-based Explainers  
6 Applications  
7 Datasets  
7.1 Synthetic datasets  
7.2 Real-world datasets  
8 Evaluation  
8.1 Quantitative Evaluation  
8.2 Qualitative Evaluation  
9 Future Directions  
10 Conclusions