Authors:
- Presents a fully updated and easily accessible introduction to statistical methods of network analysis and their implementation in R
- New edition includes a chapter covering networked experiments and an overhaul to the R code for igraph
- Accessible for researchers in other quantitative fields or practitioners in applied areas that need to analyze network data
- Includes supplementary material: sn.pub/extras
Part of the book series: Use R! (USE R)
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (11 chapters)
-
Front Matter
-
Back Matter
About this book
The new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks.
The book begins by covering tools for the manipulation of network data. Next, it addresses visualizationand characterization of networks. The book then examines mathematical and statistical network modeling. This is followed by a special case of network modeling wherein the network topology must be inferred. Network processes, both static and dynamic are addressed in the subsequent chapters. The book concludes by featuring chapters on network flows, dynamic networks, and networked experiments. Statistical Analysis of Network Data with R, 2nd Ed. has been written at a level aimed at graduate students and researchers in quantitative disciplines engaged in the statistical analysis of network data, although advanced undergraduates already comfortable with R should find the book fairly accessible as well.
Authors and Affiliations
-
Department Mathematics and Statistics, Boston University, Boston, USA
Eric D. Kolaczyk
-
RStudio, Boston, USA
Gábor Csárdi
About the authors
Eric D. Kolaczyk is a professor of statistics and a data science faculty fellow at Boston University, in the Department of Mathematics and Statistics, where he also is an affiliated faculty member in the Bioinformatics Program, the Division of Systems Engineering, and the Center for Systems Neuroscience. Currently, he serves as the director of Boston University's Hariri Institute for Computing. His publications on network-based topics, beyond the development of statistical methodology and theory, include work on applications ranging from the detection of anomalous traffic patterns in computer networks to the prediction of biological function in networks of interacting proteins to the characterization of influence of groups of actors in social networks. He is an elected fellow of the American Association for the Advancement of Science (AAAS), the American Statistical Association (ASA), and the Institute of Mathematical Statistics, an elected member of the International Statistical Institute (ISI), and an elected senior member of the Institute of Electrical and Electronics Engineers (IEEE).
Gábor Csárdi is a software engineer at RStudio, where he works on R infrastructure packages. He holds a PhD in Computer Science from Eötvös University, Hungary, and he has done postdocs at the Swiss Institute of Bioinformatics, the University of Lausanne, and Harvard University.Bibliographic Information
Book Title: Statistical Analysis of Network Data with R
Authors: Eric D. Kolaczyk, Gábor Csárdi
Series Title: Use R!
DOI: https://doi.org/10.1007/978-3-030-44129-6
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-030-44128-9Published: 03 June 2020
eBook ISBN: 978-3-030-44129-6Published: 02 June 2020
Series ISSN: 2197-5736
Series E-ISSN: 2197-5744
Edition Number: 2
Number of Pages: XIV, 228
Number of Illustrations: 19 b/w illustrations, 56 illustrations in colour
Topics: Statistics and Computing/Statistics Programs, Computer Communication Networks, Communications Engineering, Networks