Graph Neural Networks in Action

Description

Graph Neural Networks in Action by Keita Broadwater is a comprehensive guide to building, training, and deploying graph-based deep learning models. This book explores the fundamentals of graph neural networks (GNNs) and their applications across multiple industries.

It introduces the core principles of GNNs, including message passing, node embeddings, and graph convolutions for structured data learning. Graph Neural Networks in Action provides step-by-step implementation examples using popular frameworks like PyTorch Geometric and TensorFlow GNN.

Readers will learn how to model relationships in complex datasets, such as social networks, molecular structures, and recommendation systems. The book explains how to optimize GNN architectures for tasks like node classification, link prediction, and graph clustering.

Keita Broadwater presents real-world case studies showcasing GNN applications in healthcare, finance, cybersecurity, and natural language processing. Graph Neural Networks in Action covers state-of-the-art architectures, including GraphSAGE, GAT, and transformer-based graph models.

This book emphasizes best practices for handling large-scale graph data, reducing overfitting, and improving model generalization. Readers will gain practical insights into data preprocessing, graph augmentation, and scalability techniques.

It explores how GNNs integrate with reinforcement learning, self-supervised learning, and attention mechanisms for advanced AI applications. Textbook provides hands-on coding exercises to help developers build production-ready models from scratch.

Each chapter breaks down complex concepts into intuitive explanations, making GNNs accessible to beginners and experienced practitioners. The book explains evaluation metrics, hyperparameter tuning, and model deployment strategies for real-world AI systems.

With a strong focus on interpretability, performance optimization, and ethical AI, this book equips readers with cutting-edge GNN skills. Graph Neural Networks in Action is a must-read for data scientists, AI researchers, and engineers working on graph-based machine learning solutions.