Deep learning on graphs
著者
書誌事項
Deep learning on graphs
Cambridge University Press, 2021
- : hbk
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注記
Bibliography: p. 289-314
Includes index
内容説明・目次
内容説明
Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.
目次
- 1. Deep Learning on Graphs: An Introduction
- 2. Foundation of Graphs
- 3. Foundation of Deep Learning
- 4. Graph Embedding
- 5. Graph Neural Networks
- 6. Robust Graph Neural Networks
- 7. Scalable Graph Neural Networks
- 8. Graph Neural Networks for Complex Graphs
- 9. Beyond GNNs: More Deep Models for Graphs
- 10. Graph Neural Networks in Natural Language Processing
- 11. Graph Neural Networks in Computer Vision
- 12. Graph Neural Networks in Data Mining
- 13. Graph Neural Networks in Biochemistry and Healthcare
- 14. Advanced Topics in Graph Neural Networks
- 15. Advanced Applications in Graph Neural Networks.
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