Statistical Analysis of Network Data with R

✍ Scribed by Eric D. Kolaczyk, Gábor Csárdi (auth.)

• Publisher: Springer-Verlag New York
• Year: 2014
• Tongue: English
• Leaves: 214
• Series: Use R! 65
• Edition: 1
• Category: Library

✦ Synopsis

Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It 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. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).

✦ Table of Contents

Front Matter....Pages i-xiii
Introduction....Pages 1-11
Manipulating Network Data....Pages 13-28
Visualizing Network Data....Pages 29-41
Descriptive Analysis of Network Graph Characteristics....Pages 43-67
Mathematical Models for Network Graphs....Pages 69-83
Statistical Models for Network Graphs....Pages 85-109
Network Topology Inference....Pages 111-134
Modeling and Prediction for Processes on Network Graphs....Pages 135-159
Analysis of Network Flow Data....Pages 161-178
Dynamic Networks....Pages 179-195
Back Matter....Pages 197-207

✦ Subjects

Statistics and Computing/Statistics Programs; Statistics, general; Statistical Theory and Methods