1 Introduction  
2 Notation and Preliminaries  
3 Taxonomy of Models  
3.1 Graph Embedding based Graph Similarity Learning  
3.2 GNN-based Graph Similarity Learning  
3.3 Deep Graph Kernels  
4 Datasets and Evaluation  
4.1 Datasets  
4.2 Evaluation  
5 Applications  
5.1 Computational Chemistry and Biology  
5.2 Neuroscience  
5.3 Computer Security  
5.4 Computer Vision  
6 Challenges  
6.1 Various Graph Types  
6.2 Interpretability  
6.3 Few-shot Learning  
7 Conclusion