Graph Neural Networks

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.