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Deep Learning on Graphs by Yao Ma and Jiliang Tang (2021, Hardcover)

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About this product

Product Identifiers

PublisherCambridge University Press

ISBN-101108831745

ISBN-139781108831741

eBay Product ID (ePID)20050099097

Product Key Features

Number of Pages400 Pages

Publication NameDeep Learning on Graphs

LanguageEnglish

Publication Year2021

SubjectGeneral, Computer Vision & Pattern Recognition

TypeTextbook

Subject AreaComputers, Science

AuthorYAO Ma, Jiliang Tang

FormatHardcover

Dimensions

Item Height0.9 in

Item Length9.2 in

Item Width6.1 in

Additional Product Features

Intended AudienceScholarly & Professional

Reviews'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

Dewey Edition23

IllustratedYes

Dewey Decimal006.31

Table Of Content1. 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.

SynopsisThis comprehensive text on the theory and techniques of graph neural networks takes students, practitioners, and researchers from the basics to the state of the art. It systematically introduces foundational topics such as filtering pooling, robustness, and scalability and then demonstrates applications in NLP, data mining, vision and healthcare., 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.

LC Classification NumberQ325.5