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.

• Series Preface xxviiPreface xxixPart I: Conceptual Foundations and Learning Frameworks 11 Introduction to Graph Neural Networks 3K. Sangeetha and P. Solairani2 Graph Theory Foundations for Neural Network Models 43Nidhi Asthana, Divya Gautam and Gaurav Paliwal3 Message Passing Techniques in Graph-Based Learning 101Kulkarni Manjusha Manikrao and Savitha Hiremath4 Architectures Defining Graph Neural Networks: Capitalizing on the Potential of GNN's for Real-World Solutions 143A. Priyadharshini and Saju MathewPart II: Ethical Models, Scalability, and Architectural Challenges 1815 Ethical Considerations in Graph-Based Learning Models 183Aaquil Bunglowala and Gaurav Paliwal6 Graph Neural Network: Scalability Challenges in Large-Scale Graph Neural Network 243Abubakar Nadaf and Shivagonda Patil7 Challenges in Large-Scale Graph Neural Networks in Topological Indices on Family of Graphs 283Senbagamalar J. and Ramani M.S.8 Large-Scale AI Systems Leveraging Graph Neural Networks (GNNs) 315Gaurav Paliwal, Divya Gautam and Nidhi AsthanaPart III: Domain-Specific Applications of GNNs 3679 Optimizing Federated Learning Using Graph Neural Networks 369M. Indira, C. Victoria Priscilla and V. Rekha10 Scientific Computing Applications of Graph Neural Networks 411Meenakshi S., Joshika Pradeep A. P. and Rowthri M.11 Enhanced Integrated Spatio-Temporal Graph Convolutional Network for Accurate and Efficient Traffic Prediction 459Tintu George, Ginne M. James and A. Senthil Kumar12 Crowd Analytics Using Graph Neural Networks: Improving the Accuracy of People Counting 495M. Deepadharshana and S. Vijayarani13 Stratified Sampling and Graph Neural Networks for Zero-Day Attack Detection 539Karthika S., Sandhiya R. and A. Sumi14 Graph-Based Anomaly Detection for Security Applications: Techniques, Challenges, and Future Directions 579Divya Gautam, Nidhi Asthana and Gaurav Paliwal15 Graph-Based Anomaly Detection in Cybersecurity and FinTech: Advancing Threat Intelligence with Graph Neural Networks 615Sanjeev Khan, Nutan Pathania, Pawan Kumar and Vishal16 Graph Neural Networks in Healthcare 663S. Sathyanarayanan17 Graph Neural Networks for Early Prediction of Osteoporosis Disease 701T. Mathankumar and S. Vijayarani18 Learning Behavioral Patterns for Autism Prediction with Graph Neural Networks 741Deepa B. and K. S. Jeen MarselinePart IV: Research Analytics, Optimization, and Software Assurance 75919 Emerging Trends and Collaborative Networks in Indian Graph Neural Networks Research: A Bibliometric Analysis 761Muthukrishnan M., Ghouse Modin Nabeesab Mamdapur, Saravanan S. and Parasakthi D.20 Graph Neural Networks for Optimization: A New Paradigm in Complex Problem Solving 797Spelmen Vimalraj Santhanam and Vignesh Ramamoorthy H.21 Quality Assurance of Software Incorporating Graph Neural Networks for Defect Prediction 823Medhunhashini D. R., K. S. Jeen Marseline and B. Varun22 Future Research Directions in Graph Neural Networks 859Valarmathi ViswanathanReferences 902Index 905

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.

• Series Preface xxviiPreface xxixPart I: Conceptual Foundations and Learning Frameworks 11 Introduction to Graph Neural Networks 3K. Sangeetha and P. Solairani2 Graph Theory Foundations for Neural Network Models 43Nidhi Asthana, Divya Gautam and Gaurav Paliwal3 Message Passing Techniques in Graph-Based Learning 101Kulkarni Manjusha Manikrao and Savitha Hiremath4 Architectures Defining Graph Neural Networks: Capitalizing on the Potential of GNN's for Real-World Solutions 143A. Priyadharshini and Saju MathewPart II: Ethical Models, Scalability, and Architectural Challenges 1815 Ethical Considerations in Graph-Based Learning Models 183Aaquil Bunglowala and Gaurav Paliwal6 Graph Neural Network: Scalability Challenges in Large-Scale Graph Neural Network 243Abubakar Nadaf and Shivagonda Patil7 Challenges in Large-Scale Graph Neural Networks in Topological Indices on Family of Graphs 283Senbagamalar J. and Ramani M.S.8 Large-Scale AI Systems Leveraging Graph Neural Networks (GNNs) 315Gaurav Paliwal, Divya Gautam and Nidhi AsthanaPart III: Domain-Specific Applications of GNNs 3679 Optimizing Federated Learning Using Graph Neural Networks 369M. Indira, C. Victoria Priscilla and V. Rekha10 Scientific Computing Applications of Graph Neural Networks 411Meenakshi S., Joshika Pradeep A. P. and Rowthri M.11 Enhanced Integrated Spatio-Temporal Graph Convolutional Network for Accurate and Efficient Traffic Prediction 459Tintu George, Ginne M. James and A. Senthil Kumar12 Crowd Analytics Using Graph Neural Networks: Improving the Accuracy of People Counting 495M. Deepadharshana and S. Vijayarani13 Stratified Sampling and Graph Neural Networks for Zero-Day Attack Detection 539Karthika S., Sandhiya R. and A. Sumi14 Graph-Based Anomaly Detection for Security Applications: Techniques, Challenges, and Future Directions 579Divya Gautam, Nidhi Asthana and Gaurav Paliwal15 Graph-Based Anomaly Detection in Cybersecurity and FinTech: Advancing Threat Intelligence with Graph Neural Networks 615Sanjeev Khan, Nutan Pathania, Pawan Kumar and Vishal16 Graph Neural Networks in Healthcare 663S. Sathyanarayanan17 Graph Neural Networks for Early Prediction of Osteoporosis Disease 701T. Mathankumar and S. Vijayarani18 Learning Behavioral Patterns for Autism Prediction with Graph Neural Networks 741Deepa B. and K. S. Jeen MarselinePart IV: Research Analytics, Optimization, and Software Assurance 75919 Emerging Trends and Collaborative Networks in Indian Graph Neural Networks Research: A Bibliometric Analysis 761Muthukrishnan M., Ghouse Modin Nabeesab Mamdapur, Saravanan S. and Parasakthi D.20 Graph Neural Networks for Optimization: A New Paradigm in Complex Problem Solving 797Spelmen Vimalraj Santhanam and Vignesh Ramamoorthy H.21 Quality Assurance of Software Incorporating Graph Neural Networks for Defect Prediction 823Medhunhashini D. R., K. S. Jeen Marseline and B. Varun22 Future Research Directions in Graph Neural Networks 859Valarmathi ViswanathanReferences 902Index 905

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.

• Series Preface xxviiPreface xxixPart I: Conceptual Foundations and Learning Frameworks 11 Introduction to Graph Neural Networks 3K. Sangeetha and P. Solairani2 Graph Theory Foundations for Neural Network Models 43Nidhi Asthana, Divya Gautam and Gaurav Paliwal3 Message Passing Techniques in Graph-Based Learning 101Kulkarni Manjusha Manikrao and Savitha Hiremath4 Architectures Defining Graph Neural Networks: Capitalizing on the Potential of GNN's for Real-World Solutions 143A. Priyadharshini and Saju MathewPart II: Ethical Models, Scalability, and Architectural Challenges 1815 Ethical Considerations in Graph-Based Learning Models 183Aaquil Bunglowala and Gaurav Paliwal6 Graph Neural Network: Scalability Challenges in Large-Scale Graph Neural Network 243Abubakar Nadaf and Shivagonda Patil7 Challenges in Large-Scale Graph Neural Networks in Topological Indices on Family of Graphs 283Senbagamalar J. and Ramani M.S.8 Large-Scale AI Systems Leveraging Graph Neural Networks (GNNs) 315Gaurav Paliwal, Divya Gautam and Nidhi AsthanaPart III: Domain-Specific Applications of GNNs 3679 Optimizing Federated Learning Using Graph Neural Networks 369M. Indira, C. Victoria Priscilla and V. Rekha10 Scientific Computing Applications of Graph Neural Networks 411Meenakshi S., Joshika Pradeep A. P. and Rowthri M.11 Enhanced Integrated Spatio-Temporal Graph Convolutional Network for Accurate and Efficient Traffic Prediction 459Tintu George, Ginne M. James and A. Senthil Kumar12 Crowd Analytics Using Graph Neural Networks: Improving the Accuracy of People Counting 495M. Deepadharshana and S. Vijayarani13 Stratified Sampling and Graph Neural Networks for Zero-Day Attack Detection 539Karthika S., Sandhiya R. and A. Sumi14 Graph-Based Anomaly Detection for Security Applications: Techniques, Challenges, and Future Directions 579Divya Gautam, Nidhi Asthana and Gaurav Paliwal15 Graph-Based Anomaly Detection in Cybersecurity and FinTech: Advancing Threat Intelligence with Graph Neural Networks 615Sanjeev Khan, Nutan Pathania, Pawan Kumar and Vishal16 Graph Neural Networks in Healthcare 663S. Sathyanarayanan17 Graph Neural Networks for Early Prediction of Osteoporosis Disease 701T. Mathankumar and S. Vijayarani18 Learning Behavioral Patterns for Autism Prediction with Graph Neural Networks 741Deepa B. and K. S. Jeen MarselinePart IV: Research Analytics, Optimization, and Software Assurance 75919 Emerging Trends and Collaborative Networks in Indian Graph Neural Networks Research: A Bibliometric Analysis 761Muthukrishnan M., Ghouse Modin Nabeesab Mamdapur, Saravanan S. and Parasakthi D.20 Graph Neural Networks for Optimization: A New Paradigm in Complex Problem Solving 797Spelmen Vimalraj Santhanam and Vignesh Ramamoorthy H.21 Quality Assurance of Software Incorporating Graph Neural Networks for Defect Prediction 823Medhunhashini D. R., K. S. Jeen Marseline and B. Varun22 Future Research Directions in Graph Neural Networks 859Valarmathi ViswanathanReferences 902Index 905