The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph representation learning, or geometric deep learning have become one of the fastest-growing research topics in machine learning, especially deep learning. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including recommendation systems, computer vision, natural language processing, inductive logic programming, program synthesis, software mining, automated planning, cybersecurity, and intelligent transportation.

Although graph neural networks have achieved remarkable attention, it still faces many challenges when applying them into other domains, from the theoretical understanding of methods to the scalability and interpretability in a real system, and from the soundness of the methodology to the empirical performance in an application. However, as the field rapidly grows, it has been extremely challenging to gain a global perspective of the developments of GNNs.

Therefore, we feel the urgency to bridge the above gap and have a comprehensive book on this fast-growing yet challenging topic, which can benefit a broad audience including advanced undergraduate and graduate students, postdoctoral researchers, lecturers, and industrial practitioners.

This book is intended to cover a broad range of topics in graph neural networks, from the foundations to the frontiers, and from the methodologies to the applications. Our book is dedicated to introducing the fundamental concepts and algorithms of GNNs, new research frontiers of GNNs, and broad and emerging applications with GNNs.

The English version of the book is available for purchase on Amazon and Springer.





The Chinese version is available for pre-order now (50% off) at now!


“The first comprehensive book covering the full spectrum of a young, fast-growing research field, graph neural networks (GNNs), written by authoritative authors!”
---Jiawei Han (Michael Aiken Chair Professor at University of Illinois at Urbana-Champaign, ACM Fellow and IEEE Fellow)

“This book presents a comprehensive and timely survey on graph representation learning. Edited and contributed by the best group of experts in this area, this book is a must-read for students, researchers and pratictioners who want to learn anything about Graph Neural Networks.”
---Heung-Yeung ”Harry” Shum (Former Executive Vice President for Technology and Research at Microsoft Research, ACM Fellow, IEEE Fellow, FREng)

“As the new frontier of deep learning, Graph Neural Networks offer great potential to combine probabilistic learning and symbolic reasoning, and bridge knowledge-driven and data-driven paradigms, nurturing the development of third-generation AI. This book provides a comprehensive and insightful introduction to GNN, ranging from foundations to frontiers, from algorithms to applications. It is a valuable resource for any scientist, engineer and student who wants to get into this exciting field.”
---Bo Zhang (Member of Chinese Academy of Science, Professor at Tsinghua University)

“Graph Neural Networks are one of the hottest areas of machine learning and this book is a wonderful in-depth resource covering a broad range of topics and applications of graph representation learning.”
---Jure Leskovec (Associate Professor at Stanford University, and investigator at Chan Zuckerberg Biohub)

“Graph Neural Networks are an emerging machine learning model that is already taking the scientific and industrial world by storm. The time is perfect to get in on the action – and this book is a great resource for newcomers and seasoned practitioners alike! Its chapters are very carefully written by many of the thought leaders at the forefront of the area.”
---Petar Velickovic (Senior Research Scientist, DeepMind)


Copyrights and Citation

This book is a pre-publication draft of the book that will be published by Springer. The publishers have generously agreed to allow the public hosting of the pre-publication draft, which does not include the publisher's formatting or revisions. The book should be cited as follows:

author = "Wu, Lingfei and Cui, Peng and Pei, Jian and Zhao, Liang",
title = "Graph Neural Networks: Foundations, Frontiers, and Applications",
publisher = "Springer Singapore",
address = "Singapore",
pages = "725",
year = "2022",

L. Wu, P. Cui, J. Pei, and L. Zhao. Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore, 2022
All copyrights held by the authors and publishers extend to the pre-publication drafts.


Feedback, typo corrections, and comments are welcome and should be sent to Dr. Lingfei Wu at with [GNN BOOK] in the subject line.