Deep Learning on Graphs

The book "Deep Learning on Graphs" covers fundamental concepts, methods, and applications of graph neural networks, making it accessible to a wide range of readers interested in machine learning and graph analysis.

\n Format: Hardback
\n Length: 400 pages
\n Publication date: 23 September 2021
\n Publisher: Cambridge University Press
\n

Graphs have emerged as a captivating domain within the realm of machine learning, attracting significant attention and fostering a surge of interest among researchers and practitioners alike. In this comprehensive guide, we delve into the intricate world of deep learning on graphs, offering a comprehensive exploration of its fundamental concepts, cutting-edge methodologies, and diverse applications.

The book is structured into four distinct parts, meticulously designed to cater to readers with diverse backgrounds and purposes of reading. Part 1 serves as an introductory primer, providing a solid foundation in graph theory and deep learning principles. It introduces key concepts such as graph representations, node embeddings, and neural network architectures, laying the groundwork for subsequent chapters.

Part 2 delves into the most established methods in deep learning on graphs, spanning from basic to advanced settings. It covers a wide range of techniques, including graph convolutional networks (GCNs), deep walk networks, and graph neural networks (GNNs). Each method is thoroughly explained, accompanied by practical examples and real-world applications, making it accessible to both novice and experienced practitioners.

Part 3 showcases the most typical applications of deep learning on graphs. We explore a diverse array of fields, including natural language processing, computer vision, data mining, biochemistry, and healthcare. By leveraging the power of graph structures, these applications demonstrate the potential of graph neural networks to address complex problems and extract valuable insights from large-scale datasets.

Part 4 delves into the advancements and promising directions of deep learning on graphs. It highlights emerging methods, such as graph attention networks, self-supervised learning, and graph generation, that hold great potential for future research. It also provides insights into the challenges and opportunities associated with graph-based learning, fostering a deeper understanding of the field and inspiring researchers to push the boundaries of what is possible.

This book is self-contained, making it accessible to a broad spectrum of readers. Whether you are a senior undergraduate or graduate student seeking to expand your knowledge in graph-based machine learning, a practitioner or project manager eager to integrate graph neural networks into your products or platforms, or a researcher from a non-computer science background seeking to leverage graph neural networks for your discipline, this guide is your invaluable resource.

By exploring the intricacies of deep learning on graphs, this book empowers you with the skills and knowledge necessary to tackle real-world problems and drive innovation in various industries. So, whether you are a beginner or an expert in the field, take advantage of this comprehensive guide and embark on a journey of discovery into the exciting world of deep learning on graphs.

\n Weight: 586g\n
Dimension: 158 x 236 x 25 (mm)\n
ISBN-13: 9781108831741\n \n