I. INTRODUCTION  
II. BACKGROUND AND CATEGORIZATION  
A. Background of GCN  
B. Categorization of sampling methods  
III. SAMPLING METHODS  
A. Node-wise sampling method  
B. Layer-wise sampling method  
C. Subgraph-based sampling method  
D. Heterogeneous sampling method  
IV. COMPARISON AND ANALYSIS  
A. Comparison in characteristics  
B. Comparison in applications  
C. Comparison in experiments  
D. Comparison in deep models  
E. Comparison in overall  
V. CHALLENGES AND FUTURE DIRECTIONS  
VI. CONCLUSION