Overview
- Introduces the foundations and frontiers of graph neural networks
- Utilizes graph data to describe pairwise relations for real-world data from many different domains
- Summarizes the basic concepts and terminology in graph modeling
Part of the book series: Synthesis Lectures on Data Mining and Knowledge Discovery (SLDMKD)
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About this book
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.
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Keywords
Table of contents (9 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the authors
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.
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.
Bibliographic Information
Book Title: Advances in Graph Neural Networks
Authors: Chuan Shi, Xiao Wang, Cheng Yang
Series Title: Synthesis Lectures on Data Mining and Knowledge Discovery
DOI: https://doi.org/10.1007/978-3-031-16174-2
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 12
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-16173-5Published: 17 November 2022
Softcover ISBN: 978-3-031-16176-6Published: 18 November 2023
eBook ISBN: 978-3-031-16174-2Published: 16 November 2022
Series ISSN: 2151-0067
Series E-ISSN: 2151-0075
Edition Number: 1
Number of Pages: XIV, 198
Number of Illustrations: 5 b/w illustrations, 36 illustrations in colour
Topics: Graph Theory, Computer Science, general, Mathematical Applications in Computer Science, Mathematical Models of Cognitive Processes and Neural Networks, Data Mining and Knowledge Discovery