Graph Neural Networks
Editat de B. Suchitra, J. Ramkumar, S. Balamurugan, Sridaran Rajagopalen Limba Engleză Hardback – 24 noi 2026
Master the power of relational AI with this comprehensive guide, designed to seamlessly bridge the gap between foundational graph theory and the practical deployment of highly efficient, domain-aware Graph Neural Networks across industries like bioinformatics, cybersecurity, and social network analysis.
Graph Neural Networks (GNNs) represent a transformative advancement in artificial intelligence and machine learning, enabling deep learning models to efficiently process structured, relational data. As industries increasingly rely on complex networks, GNNs offer an essential toolset for extracting insights from graph-structured data. This book provides a comprehensive exploration of GNN architectures, methodologies, and real-world applications, bridging the gap between foundational research and practical deployment across diverse domains. It introduces basic principles and advanced concepts, including graph theory essentials, message-passing mechanisms, and foundational GNN architectures, and explores convolution layers, aggregation functions, sampling techniques, and training strategies across supervised and semi-supervised settings. Designed with both clarity and depth, this book lays the groundwork for understanding how GNNs effectively model relationships, hierarchies, and contextual dependencies in real-world data. The book extends to interdisciplinary contexts such as bioinformatics, cybersecurity, infrastructure analytics, and social network analysis. By bridging foundational theory with practical implementations, the book serves as a key reference for students, researchers, and AI practitioners working with graph-structured data to build trustworthy, efficient, and domain-aware GNN solutions.
Readers will find the volume:
- Offers a structured, end-to-end exploration of graph neural networks from foundational theory to cutting-edge techniques;
- Includes chapters spanning applications in healthcare, finance, transportation, cybersecurity, and recommender systems;
- Delivers practical insights into designing scalable, interpretable, and context-aware GNN architectures across real-world graph environments;
- Covers explainability, fairness, graph augmentation, anomaly detection, and temporal graph modelling in real-world contexts.
Audience
Computer scientists, data scientists, industry professionals, and AI practitioners working with non-Euclidean, graph-structured data in the finance, healthcare, and cybersecurity sectors.
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Specificații
ISBN-10: 1394422733
Pagini: 960
Ediția:1. Auflage
Editura: John Wiley & Sons, Inc.
Notă biografică
J. Ramkumar, PhD is an Associate Professor at the Department of Computer Science, School of Quantum Science, Computing and AI, Rathinam Global (Deemed to be University), Coimbatore, Tamil Nadu, India. He has published ten authored books, four edited books, 20 journal articles, 35 conference papers, and five book chapters. He specializes in advanced networks, bio-inspired optimization, machine learning, intrusion detection systems, sentiment analysis, fintech, and IoT-based security.
Sridaran Rajagopal, PhD is the Executive Dean of Academic Quality Assurance at Ganpat University, Gujarat, India. With over 30 years of academic and research experience, he has authored multiple books and published numerous research papers in internationally reputed journals, as well as nine patents, one of which was granted. His expertise includes cloud computing, cybersecurity, and software engineering.
B. Suchitra, PhD is an Assistant Professor at the Sri Krishna College of Arts and Science, Coimbatore, India, with over 13 years of experience. She has authored multiple books, secured patents related to AI-driven applications, and serves as a reviewer for internationally recognized journals. Her research covers topics including artificial intelligence, optimization algorithms, and structured data analysis.
S. Balamurugan, PhD is the Director of Research at iRCS, an Indian Technological Research and Consulting Firm. He has published 75 books, 300 papers in international journals and conferences, and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision-making for leading companies and startups.
Cuprins
Series Preface xxvii
Preface xxix
Part I: Conceptual Foundations and Learning Frameworks 1
1 Introduction to Graph Neural Networks 3
K. Sangeetha and P. Solairani
2 Graph Theory Foundations for Neural Network Models 43
Nidhi Asthana, Divya Gautam and Gaurav Paliwal
3 Message Passing Techniques in Graph-Based Learning 101
Kulkarni Manjusha Manikrao and Savitha Hiremath
4 Architectures Defining Graph Neural Networks: Capitalizing on the Potential of GNN's for Real-World Solutions 143
A. Priyadharshini and Saju Mathew
Part II: Ethical Models, Scalability, and Architectural Challenges 181
5 Ethical Considerations in Graph-Based Learning Models 183
Aaquil Bunglowala and Gaurav Paliwal
6 Graph Neural Network: Scalability Challenges in Large-Scale Graph Neural Network 243
Abubakar Nadaf and Shivagonda Patil
7 Challenges in Large-Scale Graph Neural Networks in Topological Indices on Family of Graphs 283
Senbagamalar J. and Ramani M.S.
8 Large-Scale AI Systems Leveraging Graph Neural Networks (GNNs) 315
Gaurav Paliwal, Divya Gautam and Nidhi Asthana
Part III: Domain-Specific Applications of GNNs 367
9 Optimizing Federated Learning Using Graph Neural Networks 369
M. Indira, C. Victoria Priscilla and V. Rekha
10 Scientific Computing Applications of Graph Neural Networks 411
Meenakshi S., Joshika Pradeep A. P. and Rowthri M.
11 Enhanced Integrated Spatio-Temporal Graph Convolutional Network for Accurate and Efficient Traffic Prediction 459
Tintu George, Ginne M. James and A. Senthil Kumar
12 Crowd Analytics Using Graph Neural Networks: Improving the Accuracy of People Counting 495
M. Deepadharshana and S. Vijayarani
13 Stratified Sampling and Graph Neural Networks for Zero-Day Attack Detection 539
Karthika S., Sandhiya R. and A. Sumi
14 Graph-Based Anomaly Detection for Security Applications: Techniques, Challenges, and Future Directions 579
Divya Gautam, Nidhi Asthana and Gaurav Paliwal
15 Graph-Based Anomaly Detection in Cybersecurity and FinTech: Advancing Threat Intelligence with Graph Neural Networks 615
Sanjeev Khan, Nutan Pathania, Pawan Kumar and Vishal
16 Graph Neural Networks in Healthcare 663
S. Sathyanarayanan
17 Graph Neural Networks for Early Prediction of Osteoporosis Disease 701
T. Mathankumar and S. Vijayarani
18 Learning Behavioral Patterns for Autism Prediction with Graph Neural Networks 741
Deepa B. and K. S. Jeen Marseline
Part IV: Research Analytics, Optimization, and Software Assurance 759
19 Emerging Trends and Collaborative Networks in Indian Graph Neural Networks Research: A Bibliometric Analysis 761
Muthukrishnan M., Ghouse Modin Nabeesab Mamdapur, Saravanan S. and Parasakthi D.
20 Graph Neural Networks for Optimization: A New Paradigm in Complex Problem Solving 797
Spelmen Vimalraj Santhanam and Vignesh Ramamoorthy H.
21 Quality Assurance of Software Incorporating Graph Neural Networks for Defect Prediction 823
Medhunhashini D. R., K. S. Jeen Marseline and B. Varun
22 Future Research Directions in Graph Neural Networks 859
Valarmathi Viswanathan
References 902
Index 905