"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 the prevailing challenges and opportunities"--
Author(s): Vinod Kumar, Dharmendra Singh Rajput
Publisher: Engineering Science Reference
Year: 2023
Language: English
Pages: 267
Title Page
Copyright Page
Book Series
Editorial Advisory Board
Table of Contents
Detailed Table of Contents
Foreword
Preface
Acknowledgment
Chapter 1: Fundamentals of Graph for Graph Neural Network
Chapter 2: Graph Neural Network and Its Applications
Chapter 3: Introduction to Graph Neural Network
Chapter 4: Graph Classification of Graph Neural Networks
Chapter 5: Adversarial Attacks on Graph Neural Network
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
Compilation of References
About the Contributors
Index
