1 INTRODUCTION  
2 PROBLEM DEFINITION  
3 CATEGORIZATION AND FRAMEWORKS  
3.1 Taxonomy of Graph Domain Adaptation Learning  
3.2 Taxonomy of Graph Out-of-distribution Learning  
3.3 Taxonomy of Graph Continual Learning  
4 GRAPH DOMAIN ADAPTATION LEARNING  
4.1 Semi-supervised Domain Adaptation  
4.2 Unsupervised Domain Adaptation  
4.3 Test-time Graph Transformation (Adaptation)  
5 GRAPH OUT-OF-DISTRIBUTION LEARNING  
5.1 Graph Out-of-distribution Generalization  
5.2 Graph Out-of-distribution Detection  
5.3 Open-world Graph Learning  
6 GRAPH CONTINUAL LEARNING  
6.1 Architectural Approach  
6.2 Regularization Approach  
6.3 Rehearsal Approach  
6.4 Hybrid Approach  
7 APPLICATIONS  
7.1 AI-aided Drug Discovery.  
7.2 Personalized Intelligent Recommendation Systems.  
7.3 Open-world Knowledge Exploration and Learning.  
7.4 Intelligent Transportation System Forecasting.  
8 FUTURE DIRECTIONS  
8.1 Graph Data-centric Learning under Distribution Shifts.  
8.2 Cross-modality Distribution Shift Exploration on Graph Learning.  
8.3 Comprehensive Graph Learning Task-driven Evaluation Protocols.  
8.4 Trustworthy Graph Learning Under Distribution Shifts.  
9 CONCLUSION