Overview
- Provides an overview of various research fields and challenges within network science
- Features easily accessible contributions with low-level or limited technical information, written by experts in their respective fields of network science
- Fosters inter- and cross-disciplinary collaboration and highlights perspectives for further research in network science
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (7 chapters)
Keywords
About this book
This book provides an overview of network science from the perspective of diverse academic fields, offering insights into the various research areas within network science. The authoritative contributions on statistical network analysis, mathematical network science, genetic networks, Bayesian networks, network visualisation, and systemic risk in networks explore the main questions in the respective fields: What has been achieved to date? What are the research challenges and obstacles? What are the possible interconnections with other fields? And how can cross-fertilization between these fields be promoted?
Network science comprises numerous scientific disciplines, including computer science, economics, mathematics, statistics, social sciences, bioinformatics, and medicine, among many others. These diverse research areas require and use different data-analytic and numerical methods as well as different theoretical approaches. Nevertheless, they all examine and describe interdependencies, associations, and relationships of entities in different kinds of networks.
The book is intended for researchers as well as interested readers working in network science who want to learn more about the field – beyond their own research or work niche. Presenting network science from different perspectives without going into too much technical detail, it allows readers to gain an overview without having to be a specialist in any or all of these disciplines.
Editors and Affiliations
About the editors
Francesca Biagini is a Full Professor of Applied Mathematics and Head of the Department of Mathematics, Ludwig-Maximilians-Univerität München (LMU Munich), Germany. Her main research interests include martingale methods for insurance markets, quadratic hedging methods, mathematical models for the formation of financial bubbles, and stochastic calculus for fractional Brownian motion.
Göran Kauermann is a Full Professor of Statistics at the Department of Statistics, Ludwig-Maximilians-Universität München (LMU Munich), Germany. His main research interests include applied statistics in general, including semi- and nonparametric regression models, network data analysis and data science.
Thilo Meyer-Brandis is Professor of Applied Mathematics at the Department of Mathematics, Ludwig-Maximilians-Universität München (LMU Munich), Germany. His main research interests are within the field of probability theory with applications in financial and insurance mathematics, including stochastic (partial) differential equations, Malliavin calculus, mathematical models for systemic risk, pricing and hedging in commodity and electricity markets.
Bibliographic Information
Book Title: Network Science
Book Subtitle: An Aerial View
Editors: Francesca Biagini, Göran Kauermann, Thilo Meyer-Brandis
DOI: https://doi.org/10.1007/978-3-030-26814-5
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-26813-8Published: 28 November 2019
Softcover ISBN: 978-3-030-26816-9Published: 28 November 2020
eBook ISBN: 978-3-030-26814-5Published: 19 November 2019
Edition Number: 1
Number of Pages: IX, 119
Number of Illustrations: 21 b/w illustrations, 16 illustrations in colour
Topics: Statistical Theory and Methods, Probability Theory and Stochastic Processes, Probability and Statistics in Computer Science, Complex Systems, Computer Appl. in Social and Behavioral Sciences, Statistics for Life Sciences, Medicine, Health Sciences