Deep Learning on Graphs A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare. Yao Ma (Author), Jiliang Tang (Author) 9781108831741, Cambridge University Press Hardback, published 23 September 2021 400 pages23.4 x 15.5 x 2.3 cm, 0.61 kg '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 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. 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. Subject Areas: Computer vision [UYQV], Neural networks & fuzzy systems [UYQN], Machine learning [UYQM], Natural language & machine translation [UYQL], Combinatorics & graph theory [PBV]
