Statistical Analysis of Network Data with R

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

Chapter 1 Introduction
Chapter 2 Manipulating Network Data
Chapter 3 Visualizing Network Data
Chapter 4 Descriptive Analysis of Network Graph Characteristics
Chapter 5 Mathematical Models for Network Graphs
Chapter 6 Statistical Models for Network Graphs
Chapter 7 Network Topology Inference
Chapter 8 Modeling and Prediction for Processes on Network Graphs
Chapter 9 Analysis of Network Flow Data
Chapter 10 Dynamic Networks