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

Deep Learning on Graphs

• Gebundenes Buch

Produktbeschreibung

This 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.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 400
• Erscheinungstermin: 23. September 2021
• Englisch
• Abmessung: 330mm x 264mm x 2mm
• Gewicht: 586g
• ISBN-13: 9781108831741
• ISBN-10: 1108831745
• Artikelnr.: 62216149

Autorenporträt

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.

Inhaltsangabe

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.