Temporal Network Theory
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Beschreibung
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.
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.
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
Autor*in
Petter Holme
Themen in »Temporal Network Theory«
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