Preface

In recent years, the rapid growth of networked data across fields such as social networks, molecular structures, recommendation systems, and computer vision has highlighted the need for better methods to process graph-structured data. Traditional deep learning models work well with grid-like data (such as images) and sequential data (such as text), but these models struggle with irregular structures such as graphs.

Welcome to Applied Deep Learning on Graphs, a book that addresses this challenge by exploring cutting-edge techniques at the intersection of graph theory and deep learning, offering practical strategies and insights from real-world applications at leading companies.

Drawing on our experiences implementing graph-based solutions at organizations such as Adobe, Walmart, and Meesho, the authors have seen firsthand how these techniques can transform business applications. This book provides a clear, comprehensive guide to understanding and applying graph deep learning, combining theoretical foundations with hands-on insights from large-scale industry implementations.