Graph Neural Networks: Concepts and Applications

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.

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.