Advances in Graph Neural Networks / by Chuan Shi, Xiao Wang, Cheng Yang.

Format:

Book

Author/Creator:

Shi, Chuan, Author.

Wang, Xiao, Author.

Yang, Cheng, Author.

Contributor:

SpringerLink (Online service)

Series:

Synthesis lectures on data mining and knowledge discovery 2151-0075

Synthesis Lectures on Data Mining and Knowledge Discovery, 2151-0075

Language:

English

Subjects (All):

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 Discovery.

Local Subjects:

Graph Theory.

Computer Science.

Mathematical Applications in Computer Science.

Mathematical Models of Cognitive Processes and Neural Networks.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (XIV, 198 pages 41 illustrations, 36 illustrations in color)

Edition:

1st ed. 2023.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2023.

System Details:

text file PDF

Summary:

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 .

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. .

Other Format:

Printed edition:

ISBN:

9783031161742

Access Restriction:

Restricted for use by site license.