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Graph Neural Networks: Foundations, Frontiers, and Applications

Graph Neural Networks: Foundations, Frontiers, and Applications

Paperback

DatabasesGeneral Computers

ISBN10: 9811660565
ISBN13: 9789811660566
Publisher: Springer Nature
Published: Jan 5 2023
Pages: 689
Weight: 2.21
Height: 1.45 Width: 6.14 Depth: 9.21
Language: English
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.


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