1 INTRODUCTION

2 PRELIMINARIES OF GRAPH NEURAL NETWORKS
2.1 Notations
2.2 Inner Working of Graph Neural Networks
2.3 Graph Analysis Tasks

3 PRIVACY OF GRAPH NEURAL NETWORKS
3.1 Taxonomy of Privacy Attacks
3.2 Methods of Privacy Attack on GNNs
3.3 Privacy-Preserving Graph Neural Networks
3.4 Datasets for Privacy-Preserving GNNs
3.5 Applications of Privacy Preserving GNNs
3.6 Future Research Directions of Privacy Preserving GNNs

4 ROBUSTNESS OF GRAPH NEURAL NETWORKS
4.1 Threat Models of Graph Adversarial Attacks
4.2 Graph Adversarial Attack Methods
4.3 Robust Graph Neural Networks
4.4 Applications of Robust GNNs
4.5 Future Research Directions of Robust GNNs

5 FAIRNESS OF GRAPH NEURAL NETWORKS
5.1 Bias Issues in Graph-Structured Data and Graph Neural Networks
5.2 Fairness Definitions
5.3 Fairness-Aware Graph Neural Networks
5.4 Datasets for Fair GNNs
5.5 Applications of Fair GNNs
5.6 Future Research Directions of Fair GNNs

6 EXPLAINABILITY OF GRAPH NEURAL NETWORKS
6.1 Backgrounds
6.2 Desired Qualities of Explanations
6.3 Taxonomy of Explainability of Graph Neural Networks
6.4 Datasets for Explainability of GNNs
6.5 Evaluation Metrics
6.6 Future Research Directions on Explainability of GNNs

7 CONCLUSION