Eric Kolaczyk and Gábor Csárdi provide an accessible introduction to the statistical analysis of network data using R.
The book can be used on its own and draws on a range of R packages to show how to carry out many kinds of network analysis.
Its central tool is the igraph package, and this fully revised second edition has been updated to reflect recent changes in that package.
The material moves from descriptive methods to modeling and inference, first for networks themselves and then for processes that take place on networks. It is written for graduate students and researchers in quantitative fields who analyze network data, and advanced undergraduates who are already comfortable with R should also find it approachable. The book is part of Springer’s Use R! series.
Chapters cover:
- Manipulating network data in R
- Visualizing networks
- Describing and characterizing network structure
- Mathematical and statistical models for network graphs
- Inferring network topology from data
- Modeling static and dynamic processes on networks
- Network flows and dynamic networks
- Networked experiments, a topic new to this edition
The data sets and code from the book are available in the companion sand package on CRAN.