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OverviewFull Product DetailsAuthor: Yao Ma (Michigan State University) , Jiliang Tang (Michigan State University)Publisher: Cambridge University Press Imprint: Cambridge University Press Dimensions: Width: 15.50cm , Height: 2.30cm , Length: 23.40cm Weight: 0.610kg ISBN: 9781108831741ISBN 10: 1108831745 Pages: 400 Publication Date: 23 September 2021 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: In stock  We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately. Table of Contents1. 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.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 '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 '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 Author InformationYao 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. Tab Content 6Author Website:Countries AvailableAll regions |
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