1 Introduction  
2 Notation and Background  
2.1 Notation  
2.2 Dynamic Graphs  
2.3 Hawkes Processes  
3 Graph Representation Learning  
3.1 Models for Dynamic Graphs  
3.2 Dynamic GNNs for DTDGs  
3.3 Dynamic GNNs for CTDGs  
4 Processing and Learning on Large-scale Dynamic Graphs  
4.1 Scalable GNNs for Dynamic Graphs  
4.2 Scalable Training Frameworks for Dynamic GNNs  
5 Datasets and Benchmarks Overview  
5.2 Prediction Problems  
5.3 Benchmarks  
6 Advanced Techniques in Graph Processing  
6.1 Dynamic Transfer Learning  
6.2 Pre-training Techniques for Dynamic GNNs  
7 Future Trends and Challenges  
7.1 More Complex Dynamic Graphs  
7.2 Interpretability of Dynamic GNNs  
7.3 LLMs for Dynamic Graph Learning  
8 Conclusion