Temporal Network Theory (Computational Social Sciences) - Softcover

Softcover

ISBN 10: 3031304012 ISBN 13: 9783031304019

Publisher: Springer, 2024

Synopsis

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.

About this title

About the Author

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.

From the Back Cover

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.

Edition details

• Publisher: Springer
• Publication date: 2024
• Language: English
• ISBN 10: 3031304012
• ISBN 13: 9783031304019
• Binding: Paperback
• Edition number: 2
• Number of pages: 496
• Editor: Holme Petter, Saramäki Jari