Deep Learning on Graphs
Deep Learning on Graphs
$67.00
USDDeep 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.
- Introduces deep learning techniques systematically and cohesively, with particular focus on graph neural networks
- Extended examples bridge theory and applications in areas such as NLP, vision, datamining, and healthcare
- Self-contained, chapters on basic concepts in graph theory and deep learning
Reviews & endorsements
'This timely book covers a combination of two active research areas in AI: deep learning and graphs. It serves the pressing need for researchers, practitioners, and students to learn these concepts and algorithms, and apply them in solving real-world problems. Both authors are world-leading experts in this emerging area.' Huan Liu, Arizona State University
'Deep learning on graphs is an emerging and important area of research. This book by Yao Ma and Jiliang Tang covers not only the foundations, but also the frontiers and applications of graph deep learning. This is a must-read for anyone considering diving into this fascinating area.' Shuiwang Ji, Texas A&M University
'The first textbook of Deep Learning on Graphs, with systematic, comprehensive and up-to-date coverage of graph neural networks, autoencoder on graphs, and their applications in natural language processing, computer vision, data mining, biochemistry and healthcare. A valuable book for anyone to learn this hot theme!' Jiawei Han, University of Illinois at Urbana-Champaign
'This book systematically covers the foundations, methodologies, and applications of deep learning on graphs. Especially, it comprehensively introduces graph neural networks and their recent advances. This book is self-contained and nicely structured and thus suitable for readers with different purposes. I highly recommend those who want to conduct research in this area or deploy graph deep learning techniques in practice to read this book.' Charu Aggarwal, Distinguished Research Staff Member at IBM and recipient of the W. Wallace McDowell Award
Product details
December 2021Hardback
9781108831741
400 pages
234 × 155 × 23 mm
0.61kg
Available
Table of Contents
- 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.
