Study 29
- [Study]Chapter 4. Case Studies and Real-world Application
- [Study]Chapter 3. Multimodal Data Fusion Techniques
- [Study]Chapter 2. Understanding Multimodal Data
- [Study]Chapter 1. Introduction to Multimodal Research
- [Study]Chapter 19. GNNs for Science
- [Study]Chapter 18. GNN in Computational Biology
- [Study]Chapter 17. Scaling Up GNNs
- [Study]Chapter 16. Advanced Topics on GNNs
- [Study]Chapter 15. Deep Generative Models for Graphs
- [Study]Chapter 14. Traditional Generative Models for Graphs
- [Study]Chapter 13. Community Structure in Networks
- [Study]Chapter 12. Frequent Subgraph Mining with GNNs
- [Study]Chapter 11. Reasoning over Knowledge Graphs
- [Study]Chapter 7. 제약 충족 문제
- [Study]Chapter 6, 제약 충족 문제
- [Study]Chapter 5, 대립 검색과 게임.
- [Study]Chapter 4, 복잡한 환경의 검색
- [Study]Chapter 3, 검색을 통한 문제해결
- [Study]Chapter 10. Knowledge Graph Embeddings
- [Study]Chpater 9. Theory of Graph Neural Networks
- [Study]Chapter 8. Applications of Graph Neural Networks
- [Study]Chapter 7. Graph Neural Networks 2: Design Space
- [Study]Chapyer 6. Graph Neural Networks 1: GNN Model
- [Study]Chapter 5. Label Propagation for Node Classification
- [Study]Chapter 4. Link Analysis: PageRank
- [Study]Chapter 3. Node Embeddings
- [Study]Chapter 2. Traditional Methods for ML on Graphs
- [Study]Chapter 1 & 2, 지능적 에이전트
- [Study]Chapter 1. Introduction to ML for Graphs