Introduction to Graph Neural Networks

Häftad, Engelska, 2020

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Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks.

Produktinformation

Utgivningsdatum2020-03-20
Mått191 x 235 x 12 mm
Vikt250 g
FormatHäftad
SpråkEngelska
SerieSynthesis Lectures on Artificial Intelligence and Machine Learning
Antal sidor109
FörlagSpringer International Publishing AG
ISBN9783031004599
OriginaltitelIntroduction to Graph Neural Networks

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Tillämpad matematik inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och it
Artificiell intelligens inom Data och it

Zhiyuan Liu is an associate professor in the Department of Computer Science and Technology, Tsinghua University. He got his B.E. in 2006 and his Ph.D. in 2011 from the Department of Computer Science and Technology, Tsinghua University. His research interests are natural language processing and social computation. He has published over 60 papers in international journals and conferences, including IJCAI, AAAI, ACL, and EMNLP.Jie Zhou is a second-year Masters student of the Department of Computer Science and Technology, Tsinghua University. He got his B.E. from Tsinghua University in 2016. His research interests include graph neural networks and natural language processing.

Preface.- Acknowledgments.- Introduction.- Basics of Math and Graph.- Basics of Neural Networks.- Vanilla Graph Neural Networks.- Graph Convolutional Networks.- Graph Recurrent Networks.- Graph Attention Networks.- Graph Residual Networks.- Variants for Different Graph Types.- Variants for Advanced Training Methods.- General Frameworks.- Applications -- Structural Scenarios.- Applications -- Non-Structural Scenarios.- Applications -- Other Scenarios.- Open Resources.- Conclusion.- Bibliography.- Authors' Biographies.