Responsible graph neural networks

1. Introduction to Graph Intelligence 2. Fundamentals of Graph Representations 3. Graph Embedding: Methods, Taxonomies, and Applications 4. Toward Graph Neural Networks: Essentials and Pillars 5. Graph Convolution Networks: A Journey from Start to End 6. Graph Attention Networks: A Journey from Start to End 7. Recurrent Graph Neural Networks: A Journey from Start to End 8. Graph Autoencoders: A Journey from Start to End 9. Interpretable Graph Intelligence: A Journey from Black to White Box 10. Toward Privacy Preserved Graph Intelligence: Concepts, Methods, and Applications

This book provides a comprehensive study on graph learning in cyber, focusing on graph neural networks (GNNs) and their cyber-security applications. It covers the basics, methods, practices, and advanced topics of graph learning, including deterministic, generative, and reinforcement learning. The book covers the applicability of simple and complex graphs, scalability, representative algorithms, and technical details. It is an invaluable resource for undergraduate students, graduate students, researchers, cyber analysts, and AI engineers looking to understand practical deep learning methods.