1 INTRODUCTION

2 BACKGROUND

2.1 Applications of Dynamic Graph Learning

2.2 Representation of Dynamic Graphs

2.3 Learning of Dynamic Graphs

2.4 Dynamic Graph Neural Networks

3 TAXONOMY OF DYNAMIC GNN MODELS

3.1 Discrete-Time Dynamic Graph Models

3.2 Continuous-Time Dynamic Graph Models

4 DYNAMIC GNN TRAINING FRAMEWORKS

4.1 Needs for DGNN Frameworks

4.2 Discrete-Time DGNN Frameworks

4.3 Continuous-Time DGNN Frameworks

5 DYNAMIC GNN BENCHMARKS

5.1 Datasets

5.2 Metrics

6 EXPERIMENTAL COMPARISON

6.1 Experiment Setting

6.2 Comparison of Dynamic GNN Models

6.3 Comparison of Models on Frameworks

6.4 Multi-GPU Scalability

6.5 Hyperparameter Settings

6.6 Node Classification Evaluation

7 OPEN CHALLENGES

8 CONCLUSION