大葉大學圖書館 |

Concepts and techniques of graph neural networks[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	006.3/2
書名/作者:	Concepts and techniques of graph neural networks/ edited by Vinod Kumar, Dharmendra Singh Rajput.
其他作者:	Kumar, Vinod,
出版者:	Hershey, Pennsylvania : : IGI Global,, 2023.
面頁冊數:	1 online resource (247 p.)
標題:	Neural networks (Computer science)
標題:	Graph theory - Data processing.
ISBN:	9781668469057 (electronic bk.)
ISBN:	1668469030
ISBN:	9781668469033
書目註:	Includes bibliographical references and index.
內容註:	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.
摘要、提要註:	"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"--
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-6903-3

Concepts and techniques of graph neural networks[electronic resource] /
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:

386157
Neural networks (Computer science)
Index Terms--Genre/Form:

336502
Electronic books.

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

Dewey Class. No.: 006.3/2

Concepts and techniques of graph neural networks[electronic resource] /
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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 Neural networks (Computer science) $3 386157
650 0 $a Graph theory $x Data processing. $3 561823
655 4 $a Electronic books. $2 local $3 336502
700 1 $a Kumar, Vinod, $d 1986-. $3 766265
700 1 $a Rajput, Dharmendra Singh, $d 1985-. $3 766266
710 2 $a IGI Global. $3 480906
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-6903-3