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Graph Neural Networks: Foundations, Frontiers, and Applications

Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of resea...

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Bibliographic Details
Other Authors: Wu, Lingfei (Editor), Cui, Peng (Editor), Pei, Jian (Editor), Zhao, Liang (Editor)
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2022, 2022
Edition:1st ed. 2022
Subjects:
Artificial Intelligence / Data Processing
Machine Learning
Models Of Computation
Computer Science
Data Mining
Theory And Algorithms For Application Domains
Data Mining And Knowledge Discovery
Automated Pattern Recognition
Pattern Recognition Systems
Data Science
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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.

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