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| 001 | 000046155619 | |
| 005 | 20230804090640 | |
| 008 | 230803s2022 si a b 000 0 eng d | |
| 020 | ▼a 9789811660535 | |
| 020 | ▼a 9811660530 | |
| 035 | ▼a (KERIS)BIB000016196779 | |
| 040 | ▼a 211029 ▼c 211029 ▼d 211009 | |
| 050 | 4 | ▼a QA76.87 |
| 082 | 0 4 | ▼a 006.3/2 ▼2 23 |
| 084 | ▼a 006.32 ▼2 DDCK | |
| 090 | ▼a 006.32 ▼b G766 | |
| 245 | 0 0 | ▼a Graph neural networks : ▼b foundations, frontiers, and applications / ▼c Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, editors. |
| 260 | ▼a Singapore : ▼b Springer, ▼c 2022. | |
| 300 | ▼a xxxvi, 689 p. : ▼b ill. (some col.) ; ▼c 24 cm. | |
| 504 | ▼a Includes bibliographical references (p. 595-689). | |
| 650 | 0 | ▼a Neural networks (Computer science). |
| 700 | 1 | ▼a Wu, Lingfei. |
| 700 | 1 | ▼a Cui, Peng. |
| 700 | 1 | ▼a Pei, Jian. |
| 700 | 1 | ▼a Zhao, Liang. |
| 945 | ▼a ITMT |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Science & Engineering Library/Sci-Info(Stacks2)/ | Call Number 006.32 G766 | Accession No. 121263733 | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Table of Contents
Chapter 1. Representation Learning.- Chapter 2. Graph Representation Learning.- Chapter 3. Graph Neural Networks.- Chapter 4. Graph Neural Networks for Node Classification.- Chapter 5. The Expressive Power of Graph Neural Networks.- Chapter 6. Graph Neural Networks: Scalability.- Chapter 7. Interpretability in Graph Neural Networks.- Chapter 8. "Graph Neural Networks: Adversarial Robustness".- Chapter 9. Graph Neural Networks: Graph Classification.- Chapter 10. Graph Neural Networks: Link Prediction.- Chapter 11. Graph Neural Networks: Graph Generation.- Chapter 12. Graph Neural Networks: Graph Transformation.- Chapter 13. Graph Neural Networks: Graph Matching.- Chapter 14. "Graph Neural Networks: Graph Structure Learning". Chapter 15. Dynamic Graph Neural Networks.- Chapter 16. Heterogeneous Graph Neural Networks.- Chapter 17. Graph Neural Network: AutoML.- Chapter 18. Graph Neural Networks: Self-supervised Learning.- Chapter 19. Graph Neural Network in Modern Recommender Systems.- Chapter 20. Graph Neural Network in Computer Vision.- Chapter 21. Graph Neural Networks in Natural Language Processing.- Chapter 22. Graph Neural Networks in Program Analysis.- Chapter 23. Graph Neural Networks in Software Mining.- Chapter 24. "GNN-based Biomedical Knowledge Graph Mining in Drug Development".- Chapter 25. "Graph Neural Networks in Predicting Protein Function and Interactions".- Chapter 26. Graph Neural Networks in Anomaly Detection.- Chapter 27. Graph Neural Networks in Urban Intelligence.
