1 INTRODUCTION  
1.1 Formal Problem Definition  
1.2 Challenges  
1.3 Our Contributions  
1.4 Relationship with Deep Generative Models  
1.5 Relationship with Existing Surveys  
1.6 Outline of the Survey  

2 UNCONDITIONAL DEEP GENERATIVE MODELS FOR GRAPH GENERATION  
2.1 Sequential generating  
2.2 One-shot generating  

3 CONDITIONAL DEEP GENERATIVE MODELS FOR GRAPH GENERATION  
3.1 Conditioning on graphs  
3.2 Conditioning on sequence  
3.3 Conditioning on semantic context  

4 EVALUATION METRICS FOR DEEP GRAPH GENERATION  
4.1 General evaluation for deep graph generation  
4.2 Evaluation for conditional deep graph generation  

5 APPLICATIONS  
5.1 Molecule generation  
5.2 Protein structure modeling  
5.3 Semantic parsing  
5.4 Code modeling  
5.5 Pseudo-industrial SAT instance generation  

6 FUTURE OPPORTUNITIES  

7 CONCLUSION