Graph Neural Networks: Foundations, Frontiers, and Applications

Deep Learning models are not immediately applicable to graph-structured data, such as text. This has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. Graph Neural Networks (GNNs) have performed remarkably on these tasks, but still face many challenges. This book provides a comprehensive introduction to GNNs, discussing the goals of graph representation learning, reviewing the history, current developments, and future directions of GNNs, presenting and reviewing fundamental methods and theories, and describing various frontiers built on GNNs. It is suitable for a wide audience, including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, and industrial and government practitioners.

Format: Paperback / softback
Length: 689 pages
Publication date: 05 January 2023
Publisher: Springer Verlag, Singapore

Artificial intelligence (AI) research is currently centered around deep learning models, which have proven to be highly effective for processing Euclidean data, such as images and sequences. However, when it comes to graph-structured data, such as text, deep learning techniques are not immediately applicable. This gap has sparked a surge of research into deep learning on graphs, encompassing various aspects such as graph representation learning, graph generation, and graph classification.

Graph neural networks (GNNs), a subfield of deep learning, have achieved remarkable results in these tasks, particularly in social networks, bioinformatics, and medical informatics. GNNs are designed to process graph data, which consists of nodes and edges that represent relationships between entities. They utilize neural networks with specialized layers to learn the features of the graph and make predictions or decisions based on the graph structure.

Despite these successes, GNNs still face several challenges. These challenges range from foundational methodologies to a deeper theoretical understanding of the power of graph representation learning. This book aims to provide a comprehensive introduction to GNNs.

In the first part, the book discusses the goals of graph representation learning and explores the historical background, current developments, and future directions of GNNs. It provides an overview of the different types of graph representations and the challenges associated with each. The book also discusses the importance of graph data in various applications and highlights the potential benefits of using GNNs in these domains.

The second part of the book presents and reviews fundamental methods and theories concerning GNNs. It covers topics such as graph convolutional networks (GCNs), node embedding techniques, graph attention mechanisms, and graph regularization. The book explains how these methods are used to learn the features of the graph and how they contribute to the performance of GNNs. It also discusses the limitations and challenges of these methods and provides insights into how they can be improved.

The third part of the book describes various frontiers that are built on top of GNNs. It covers topics such as multi-graph learning, graph fusion, and graph optimization. The book discusses how GNNs can be used to handle complex graph structures, such as social networks with multiple types of relationships, and how they can be integrated with other AI techniques such as natural language processing and computer vision.

The book concludes with an overview of recent developments in a number of applications using GNNs. It provides examples of how GNNs have been used in social network analysis, drug discovery, medical imaging, and traffic prediction. The book also discusses the potential applications of GNNs in other fields such as finance, logistics, and energy management.

This book is designed for a wide audience, including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications. The book is written in a clear and concise manner, with ample illustrations and examples to help readers understand the concepts and techniques discussed.

In conclusion, deep learning on graphs, particularly GNNs, has shown significant promise in processing graph-structured data. This book provides a comprehensive introduction to GNNs, covering their goals, fundamental methods, frontiers, and recent applications. It is an essential resource for anyone interested in learning more about this exciting field and its potential applications.

Weight: 1098g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811660566
Edition number: 1st ed. 2022