1 INTRODUCTION

2 DEFINITION AND NOTATION
2.1 Term Definitions
2.2 Notations

3 FRAMEWORK AND CATEGORIZATION
3.1 Unified Framework and Mathematical Formulation of Graph Self-Supervised Learning
3.2 Taxonomy of Graph Self-supervised Learning
3.3 Taxonomy of Self-Supervised Training Schemes
3.4 Taxonomy of Downstream Tasks

4 GENERATION-BASED METHODS
4.1 Feature Generation
4.2 Structure Generation

5 AUXILIARY PROPERTY-BASED METHODS
5.1 Auxiliary Property Classification
5.2 Auxiliary Property Regression

6 CONTRAST-BASED METHODS
6.1 Graph Augmentations
6.2 Graph Contrastive Learning
6.3 Mutual Information Estimation

7 HYBRID METHODS

8 EMPIRICAL STUDY

9 PRACTICAL APPLICATIONS

10 FUTURE DIRECTIONS

11 CONCLUSION