Overview
- Highlights recent developments in the interface with machine learning
- Covers all trending theoretical topics
- Contains contributions gathered from the top international researchers in the field
Part of the book series: Computational Social Sciences (CSS)
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Table of contents (22 chapters)
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Front Matter
Keywords
- time series analysis
- computational social science
- computational neuroscience
- big data and network theory
- time varying networks
- epidemic spreading processes
- network theory and modeling frameworks and tools
- measures of temporal network structure
- network theory and mesoscopic structures
- network theory and dynamic processes
- network epidemic spreading
- complexity
- machine learning
About this book
This book focuses on the theoretical side of temporal network research and gives an overview of the state of the art in the field. Curated by two pioneers in the field who have helped to shape it, the book contains contributions from many leading researchers. Temporal networks fill the border area between network science and time-series analysis and are relevant for epidemic modeling, optimization of transportation and logistics, as well as understanding biological phenomena.
Over the past 20 years, network theory has proven to be one of the most powerful tools for studying and analyzing complex systems. Temporal network theory is perhaps the most recent significant development in the field in recent years, with direct applications to many of the “big data” sets. This book appeals to students, researchers, and professionals interested in theory and temporal networks—a field that has grown tremendously over the last decade.
This second edition of Temporal NetworkTheory extends the first with three chapters highlighting recent developments in the interface with machine learning.
Editors and Affiliations
About the editors
Petter Holme is a professor of network science at the Department of Computer Science, Aalto University, Finland. His research interests cover many aspects of network science—from data science to theory. He has about 200 scientific publications, including about 30 on temporal networks.
Jari Saramäki is a professor of computational science at Aalto University, Finland. His research focuses on complex systems and networks, with applications ranging from computational social science to network neuroscience and biomedicine.
Bibliographic Information
Book Title: Temporal Network Theory
Editors: Petter Holme, Jari Saramäki
Series Title: Computational Social Sciences
DOI: https://doi.org/10.1007/978-3-031-30399-9
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-30398-2Published: 21 November 2023
Softcover ISBN: 978-3-031-30401-9Due: 22 December 2023
eBook ISBN: 978-3-031-30399-9Published: 20 November 2023
Series ISSN: 2509-9574
Series E-ISSN: 2509-9582
Edition Number: 2
Number of Pages: XI, 485
Number of Illustrations: 20 b/w illustrations, 120 illustrations in colour
Topics: Applications of Graph Theory and Complex Networks, Computational Social Sciences, Complexity, Complex Systems