Central Library

Normal view MARC view ISBD view

Advances in Graph Neural Networks [electronic resource] / by Chuan Shi, Xiao Wang, Cheng Yang.

By: Shi, Chuan [author.].
Contributor(s): Wang, Xiao [author.] | Yang, Cheng [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Data Mining and Knowledge Discovery: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2023Edition: 1st ed. 2023.Description: XIV, 198 p. 41 illus., 36 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031161742.Subject(s): Graph theory | Computer science | Computer science -- Mathematics | Neural networks (Computer science)  | Data mining | Graph Theory | Computer Science | Mathematical Applications in Computer Science | Mathematical Models of Cognitive Processes and Neural Networks | Data Mining and Knowledge DiscoveryAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 511.5 Online resources: Click here to access online
Contents:
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. .
In: Springer Nature eBookSummary: 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. In addition, this book: Provides a comprehensive introduction to the foundations and frontiers of graph neural networks and also summarizes the basic concepts and terminology in graph modeling Utilizes graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology Presents heterogeneous graph representation learning alongside homogeneous graph representation and Euclidean graph neural networks methods .
    average rating: 0.0 (0 votes)
No physical items for this record

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. .

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. In addition, this book: Provides a comprehensive introduction to the foundations and frontiers of graph neural networks and also summarizes the basic concepts and terminology in graph modeling Utilizes graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology Presents heterogeneous graph representation learning alongside homogeneous graph representation and Euclidean graph neural networks methods .

There are no comments for this item.

Log in to your account to post a comment.