Statistical Analysis of Network Data with R

  • 3h 53m
  • Eric D. Kolaczyk, Gábor Csárdi
  • Springer
  • 2014
  • Comprehensively covers use of R software in the analysis of both Static and Dynamic Networks
  • Many traditional and contemporary modeling and prediction methods covered, including kernel, nearest neighbor, and markov models
  • This book aligns closely with the scope and orientation of Eric Kolaczyk's widely popular STS volume Statistical Analysis of Networks

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).

About the Authors

Eric D. Kolaczyk is a professor of statistics, and Director of the Program in Statistics, in the Department of Mathematics and Statistics at Boston University, where he also is an affiliated faculty member in the Bioinformatics Program, the Division of Systems Engineering, and the Program in Computational Neuroscience. 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 Statistical Association (ASA) and an elected senior member of the Institute of Electrical and Electronics Engineers (IEEE).

Gábor Csárdi is a research associate at the Department of Statistics at Harvard University, Cambridge, Mass. He holds a PhD in Computer Science from Eötvös University, Hungary. His research includes applications of network analysis in biology and social sciences, bioinformatics and computational biology, and graph algorithms. He created the igraph software package in 2005 and has been one of the lead developers since then.

In this Book

  • Introduction
  • Manipulating Network Data
  • Visualizing Network Data
  • Descriptive Analysis of Network Graph Characteristics
  • Mathematical Models for Network Graphs
  • Statistical Models for Network Graphs
  • Network Topology Inference
  • Modeling and Prediction for Processes on Network Graphs
  • Analysis of Network Flow Data
  • Dynamic Networks