Heterogeneous Graph Representation Learning and Applications

Heterogeneous graph representation learning (HGRL) is a challenging task that requires incorporating heterogeneous structural and content information of nodes. This book provides a comprehensive survey of current developments in HGRL, including theoretical models and real applications, with two major objectives: to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this field, and to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems.

Format: Paperback / softback
Length: 318 pages
Publication date: 01 February 2023
Publisher: Springer Verlag, Singapore

Representation learning in heterogeneous graphs (HG) is a crucial task aimed at providing meaningful vector representations for each node, facilitating downstream applications like link prediction, personalized recommendation, node classification, and more. However, this task poses significant challenges due to the need to incorporate heterogeneous structural information, encompassing various node and edge types, as well as consider heterogeneous attributes or types of content associated with each node. Despite considerable advancements in homogeneous and heterogeneous graph embedding, attributed graph embedding, and graph neural networks, few approaches are capable of simultaneously and effectively handling both heterogeneous structural information and the diverse content information of each node.

In this book, we offer a comprehensive survey of the latest developments in HG representation learning. We delve into theoretical models and real-world applications showcased at top conferences and journals, including TKDE, KDD, WWW, IJCAI, and AAAI. Our primary objectives are to provide researchers with a solid understanding of the fundamental issues in this rapidly evolving field and to present the state-of-the-art research on applying heterogeneous graphs to model real systems and learn structural features of interaction systems.

To the best of our knowledge, this book is the first to summarize the latest advancements and present cutting-edge research on heterogeneous graph representation learning. It is designed to be accessible to readers with a basic grasp of computer science, data mining, and machine learning. By exploring the insights and methodologies presented herein, readers will gain valuable insights into the field and be well-equipped to contribute to the ongoing development of HG representation learning.

Weight: 522g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811661686
Edition number: 1st ed. 2022