Overview
- Exposes readers to a novel research perspective regarding dynamic network representation
- Presents four dynamic network representation methods based on latent factorization of tensors
- Accomplishes accurate and effective representation for high-dimensional and incomplete dynamic networks
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (6 chapters)
Keywords
About this book
A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes’ various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge.
In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency.
Authors and Affiliations
About the authors
Hao Wu received a Ph.D. degree in Computer Science from the University of Chinese Academy of Sciences, Beijing, China, in 2022. He is currently an Associate Professor of Data Science with the College of Computer and Information Science, Southwest University, Chongqing, China. His research interests include big data analytics and tensor methods.
Xuke Wu is currently pursuing a Ph.D. degree from the College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China. His current research interests include data mining and intelligent transportation systems.
Xin Luo received a Ph.D. degree in computer science from Beihang University, Beijing, China, in 2011. He is currently a Professor of Data Science and Computational Intelligence with the College of Computer and Information Science, Southwest University, Chongqing, China. He has authored or coauthored over 200 papers (including over 90 IEEE Transactions papers) in the areas of his interests. His research interests include big data analysis and intelligent control.
Bibliographic Information
Book Title: Dynamic Network Representation Based on Latent Factorization of Tensors
Authors: Hao Wu, Xuke Wu, Xin Luo
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-981-19-8934-6
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. 2023
Softcover ISBN: 978-981-19-8933-9Published: 08 March 2023
eBook ISBN: 978-981-19-8934-6Published: 07 March 2023
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: VIII, 80
Number of Illustrations: 4 b/w illustrations, 16 illustrations in colour
Topics: Data Structures and Information Theory, Artificial Intelligence, Statistics, general