Graph Machine Learning

Erscheinungsjahr 2027
ISBN: 978-981-9258-89-5
Verlag: Springer Singapore

Buch, Englisch, Format (B × H): 155 mm x 235 mm

Graph machine learning is rapidly reshaping how we model complex relationships—and this book offers one of the most comprehensive, practice oriented guides to mastering it from the ground up. Designed for readers who want both conceptual clarity and hands on capability, it distills the essential foundations of graph representation, graph embedding, and graph neural networks into a clear, structured learning path.

Across fifteen chapters, the book moves from fundamental graph concepts to advanced Graph Neural Network (GNN) architectures, trustworthy graph learning, spectral methods, heterogeneous graphs, and emerging graph foundation models. It not only explains the design logic behind modern graph learning algorithms but also reveals why certain models succeed—or fail—across real world tasks. Readers will explore practical scenarios in social recommendation, financial risk control, and scientific intelligence, gaining the ability to translate theory into effective solutions. The book also highlights frontier directions such as dynamic graphs, hypergraphs, large scale graph learning, and multimodal integration.

This book is ideal for university students, engineers, and technical professionals seeking a rigorous yet accessible entry point into graph machine learning. With its combination of conceptual frameworks, platform tools, code implementations, and application case studies, it equips readers to build, optimize, and deploy graph models with confidence—requiring only basic machine learning literacy as a starting point.

Zielgruppe

Graduate

Autoren/Hrsg.

Shi, Chuan

Yang, Cheng

Wang, Xiao

Zhang, Zhiqiang

Inhaltsverzeichnis

Chapter 1 Introduction to Graph Machine Learning.- Chapter 2 Graph Machine Learning Based on Feature Engineering.- Chapter 3 Graph Embedding.- Chapter 4 Introduction to Graph Neural Networks.- Chapter 5 Advanced Graph Neural Networks.- Chapter 6 Overview of Frontiers in Graph Machine Learning.- Chapter 7 Heterogeneous Graph Machine Learning.- Chapter 8 Spectral-Domain Graph Machine Learning.- Chapter 9 Trustworthy Graph Neural Networks.- Chapter 10 Graph Foundation Models.- Chapter 11 Graph Machine Learning Platforms.- Chapter 12 Graph Machine Learning Practice.- Chapter 13 Applications of Graph Machine Learning in Recommendation.- Chapter 14 Application of Graph Machine Learning in Risk Control.- Chapter 15 Applications of Graph Machine Learning in Science.

Über Autor(innen)

Chuan Shi is a professor at the School of Computer Science, Beijing University of Posts and Telecommunications, and a Changjiang Scholar Professor appointed by the Ministry of Education. His main research areas include graph machine learning, artificial intelligence, and scientific intelligence. He has published over 100 papers in CCF A-level journals and conferences, four English monographs, and has received over 29,000 citations on Google Scholar. His research achievements have won awards such as the first prize of the Science and Technology Progress Award of the Chinese Institute of Electronics and the Second Prize of the Beijing Natural Science Award. He has received titles such as Beijing Higher Education Teacher Morality Pioneer, Outstanding Ideological and Political Teacher, and China Intelligent Computing Innovation Figure.

Cheng Yang is an associate professor at the School of Computer Science, Beijing University of Posts and Telecommunications. He has long been engaged in research in data mining and natural language processing, publishing over 50 CCF A-level papers, with over 22,000 citations on Google Scholar. He received the Wu Wenjun Youth Science and Technology Award from the Chinese Association for Artificial Intelligence and was selected for the China Association for Science and Technology's Young Talent Support Project.

Xiao Wang is a professor and doctoral supervisor at Beihang University, China. His research areas include artificial intelligence, data mining, and machine learning. He has published over 100 papers, with over 19,000 citations on Google Scholar. The research findings have won the second prize of the National Natural Science Award, the first prize of the Natural Science Award of the Ministry of Education, and the first prize of the Science and Technology Progress Award of the Chinese Institute of Electronics.

Zhiqiang Zhang is the Technical Director of the Foundation Intelligence Department at Ant Group. He has long been engaged in research on large language models and graph machine learning, and has published over 100 CCF Class A papers. He has received the First Prize for Scientific and Technological Progress from the Chinese Association for Artificial Intelligence (Wu Wenjun Award) and the first prize of the Science and Technology Progress Award of the Chinese Institute of Electronics.