Overview
- Provides a comprehensive survey of heterogeneous graph representation learning
- Written by experts in the fields of data mining and machine learning
- Demonstrates effective applications of heterogeneous graphs
Part of the book series: Artificial Intelligence: Foundations, Theory, and Algorithms (AIFTA)
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Table of contents (11 chapters)
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Front Matter
Keywords
About this book
In this book, we provide a comprehensive survey of current developments in HG representation learning.More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.
Authors and Affiliations
About the authors
Xiao Wang is the assistant professor in School of Computer Sciences of Beijing University of Posts and Telecommunications. He was a postdoc in the Department of Computer Science and Technology at Tsinghua University. He got his Ph.D. in the School of Computer Science and Technology at Tianjin University and a joint-training Ph.D. at Washington University in St. Louis. The main research interests include data mining, machine learning, artificial intelligence and big data analysis. He has published more than 50 refereed papers, including top journals and conferences in data mining, such as IEEE TKDE, KDD, AAAI, IJCAI, and WWW. He also serves as SPC/PC member and Reviewer of several high-level international conferences, e.g., KDD, AAAI, IJCAI, and journals, e.g., IEEE TKDE.
Philip S. Yu's main research interests include big data, data mining (especially on graph/network mining), social network, privacy preserving data publishing, data stream, database systems, and Internet applications and technologies. He is a Distinguished Professor in the Departmentof Computer Science at UIC and also holds the Wexler Chair in Information and Technology. Before joining UIC, he was with IBM Thomas J. Watson Research Center, where he was manager of the Software Tools and Techniques department. Dr. Yu has published more than 1,300 papers in refereed journals and conferences with more than 133,000 citations and an H-index of 169. He holds or has applied for more than 300 US patents. Dr. Yu is a Fellow of the ACM and the IEEE. He is the recepient of ACM SIGKDD 2016 Innovation Award and the IEEE Computer Society's 2013 Technical Achievement Award.
Bibliographic Information
Book Title: Heterogeneous Graph Representation Learning and Applications
Authors: Chuan Shi, Xiao Wang, Philip S. Yu
Series Title: Artificial Intelligence: Foundations, Theory, and Algorithms
DOI: https://doi.org/10.1007/978-981-16-6166-2
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
Hardcover ISBN: 978-981-16-6165-5Published: 31 January 2022
Softcover ISBN: 978-981-16-6168-6Published: 01 February 2023
eBook ISBN: 978-981-16-6166-2Published: 30 January 2022
Series ISSN: 2365-3051
Series E-ISSN: 2365-306X
Edition Number: 1
Number of Pages: XX, 318
Number of Illustrations: 1 b/w illustrations
Topics: Data Mining and Knowledge Discovery, Machine Learning, Data Structures and Information Theory, Artificial Intelligence