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Concepts and techniques of graph neural networks

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Concepts and techniques of graph neural networks/ edited by Vinod Kumar, Dharmendra Singh Rajput.
其他作者:	Rajput, Dharmendra Singh,
出版者:	Hershey, Pennsylvania :IGI Global, : 2023.,
面頁冊數:	1 online resource (247 p.)
標題:	Graph theory - Data processing. -
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-6903-3
ISBN:	9781668469057 (electronic bk.)

Concepts and techniques of graph neural networks
Concepts and techniques of graph neural networks[electronic resource] /edited by Vinod Kumar, Dharmendra Singh Rajput. - Hershey, Pennsylvania :IGI Global,2023. - 1 online resource (247 p.)

Includes bibliographical references and index.

Chapter 1. Fundamentals of graph for graph neural network -- Chapter 2. Graph neural network and its applications -- Chapter 3. Introduction to graph neural network: types and applications -- Chapter 4. Graph classification of graphneural networks -- Chapter 5. Adversarial attacks on graph neural network: techniques and countermeasures -- Chapter 6. Fundamental concepts in graph attention networks -- Chapter 7. Graph convolutional neural networks for link prediction in social networks -- Chapter 8. Study and analysis of visual saliency applications using graph neural networks -- Chapter 9. Application and some fundamental study of GNN in forecasting -- Chapter 10. Applications of GNNs and m-health for disease tracking -- Chapter 11. A comprehensive study on student academic performance predictions using graph neural network -- Chapter 12. Methods and applications of graph neural networks for fake news detection using AI-inspired algorithms -- Chapter 13. Comprehensive study of face recognition using feature extraction and fusion face technique.

"This book will aim to provide stepwise discussion; exhaustive literature review; detailed analysis and discussion; rigorous experimentation results, application-oriented approach that will be demonstrated with respect to applications of Graph Neural Network (GNN). It will be written to develop the understanding of concepts and techniques on GNN and to establish the familiarity of different real applications in various domains for GNN. Moreover, it will also cover theprevailing challenges and opportunities"--

ISBN: 9781668469057 (electronic bk.)Subjects--Topical Terms:

569117
Graph theory
--Data processing.Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: QA76.87 / .C66 2023e

Dewey Class. No.: 006.3/2

Concepts and techniques of graph neural networks
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245 0 0 $a Concepts and techniques of graph neural networks $h [electronic resource] / $c edited by Vinod Kumar, Dharmendra Singh Rajput.
260 $a Hershey, Pennsylvania : $b IGI Global, $c 2023.
300 $a 1 online resource (247 p.)
504 $a Includes bibliographical references and index.
505 0 $a Chapter 1. Fundamentals of graph for graph neural network -- Chapter 2. Graph neural network and its applications -- Chapter 3. Introduction to graph neural network: types and applications -- Chapter 4. Graph classification of graphneural networks -- Chapter 5. Adversarial attacks on graph neural network: techniques and countermeasures -- Chapter 6. Fundamental concepts in graph attention networks -- Chapter 7. Graph convolutional neural networks for link prediction in social networks -- Chapter 8. Study and analysis of visual saliency applications using graph neural networks -- Chapter 9. Application and some fundamental study of GNN in forecasting -- Chapter 10. Applications of GNNs and m-health for disease tracking -- Chapter 11. A comprehensive study on student academic performance predictions using graph neural network -- Chapter 12. Methods and applications of graph neural networks for fake news detection using AI-inspired algorithms -- Chapter 13. Comprehensive study of face recognition using feature extraction and fusion face technique.
520 $a "This book will aim to provide stepwise discussion; exhaustive literature review; detailed analysis and discussion; rigorous experimentation results, application-oriented approach that will be demonstrated with respect to applications of Graph Neural Network (GNN). It will be written to develop the understanding of concepts and techniques on GNN and to establish the familiarity of different real applications in various domains for GNN. Moreover, it will also cover theprevailing challenges and opportunities"-- $c Provided by publisher.
650 0 $a Graph theory $x Data processing. $3 569117
650 0 $a Neural networks (Computer science) $3 528588
655 4 $a Electronic books. $2 local $3 554714
700 1 $a Rajput, Dharmendra Singh, $d 1985-. $3 1427659
700 1 $a Kumar, Vinod, $d 1986-. $3 1427658
710 2 $a IGI Global. $3 805187
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-6903-3