Deep Learning on Graphs

Inbunden, Engelska, 2021

AvYao Ma,Jiliang Tang,Yao (Michigan State University) Ma,Jiliang (Michigan State University) Tang

819 kr

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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.

Produktinformation

Utgivningsdatum2021-09-23
Mått155 x 234 x 23 mm
Vikt610 g
FormatInbunden
SpråkEngelska
Antal sidor400
FörlagCambridge University Press
ISBN9781108831741

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Yao Ma is a PhD student of the Department of Computer Science and Engineering at Michigan State University (MSU). He is the recipient of the Outstanding Graduate Student Award and FAST Fellowship at MSU. He has published papers in top conferences such as WSDM, ICDM, SDM, WWW, IJCAI, SIGIR and KDD, which have been cited hundreds of times. He is the leading organizer and presenter of tutorials on GNNs at AAAI'20, KDD'20 and AAAI'21, which received huge attention and wide acclaim. He has served as Program Committee Members/Reviewers in many well-known conferences and magazines such as AAAI, BigData, IJCAI, TWEB, TKDD and TPAMI. Jiliang Tang is Assistant Professor in the Department of Computer Science and Engineering at Michigan State University. Previously, he was a research scientist in Yahoo Research. He received the 2020 SIGKDD Rising Star Award, 2020 Distinguished Withrow Research Award, 2019 NSF Career Award, the 2019 IJCAI Early Career Invited Talk and 7 best paper (runnerup) awards. He has organized top data science conferences including KDD, WSDM and SDM, and is associate editor of the TKDD journal. His research has been published in highly ranked journals and top conferences, and received more than 12,000 citations with h-index 55 and extensive media coverage.

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.