Chuan Shi: Advances in Graph Neural Networks

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Cover: 9783031161766 | Advances in Graph Neural Networks | Chuan Shi (u. a.) | Taschenbuch

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Sprache: Englisch

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Beschreibung

This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.

Über den Autor

Chuan Shi, PhD., is a Professor and Deputy Director of Beijing Key Lab of Intelligent Telecommunications Software and Multimedia at the Beijing University of Posts and Telecommunications. He received his B.S. from Jilin University in 2001, his M.S. from Wuhan University in 2004, and his Ph.D. from the ICT of Chinese Academic of Sciences in 2007. His research interests include data mining, machine learning, and evolutionary computing. He has published more than 100 papers in refereed journals and conferences.

Xiao Wang, Ph.D., is an Associate Professor in the School of Computer Science at the Beijing University of Posts and Telecommunications. He received his Ph.D. from the School of Computer Science and Technology at Tianjin University in 2016. He was a postdoctoral researcher in the Department of Computer Science and Technology at Tsinghua University. His current research interests include data mining, social network analysis, and machine learning. He has published more than 70 papers in refereed journals and conferences.

Cheng Yang, Ph.D., is an Associate Professor at the Beijing University of Posts and Telecommunications. He received his B.E. and Ph.D. from Tsinghua University in 2014 and 2019, respectively. His research interests include natural language processing and network representation learning. He has published more than 20 top-level papers in international journals and conferences including ACM TOIS, EMNLP, IJCAI, and AAAI.

Inhaltsverzeichnis

Introduction.- Fundamental Graph Neural Networks.- Homogeneous Graph Neural Networks.- Heterogeneous Graph Neural Networks.- Dynamic Graph Neural Networks.- Hyperbolic Graph Neural Networks.- Distilling Graph Neural Networks.- Platforms and Practice of Graph Neural Networks.- Future Direction and Conclusion.- References.

Erscheinungsjahr:	2023
Fachbereich:	Allgemeines
Genre:	Mathematik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Seiten:	212
Reihe:	Synthesis Lectures on Data Mining and Knowledge Discovery
Inhalt:	xiv 198 S. 5 s/w Illustr. 36 farbige Illustr. 198 p. 41 illus. 36 illus. in color.
ISBN-13:	9783031161766
ISBN-10:	3031161769
Sprache:	Englisch
Ausstattung / Beilage:	Paperback
Einband:	Kartoniert / Broschiert
Autor:	Shi, Chuan Yang, Cheng Wang, Xiao
Auflage:	1st ed. 2023
Hersteller:	Springer International Publishing Synthesis Lectures on Data Mining and Knowledge Discovery
Maße:	240 x 168 x 12 mm
Von/Mit:	Chuan Shi (u. a.)
Erscheinungsdatum:	18.11.2023
Gewicht:	0,365 kg

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