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Statistical analysis of network data with R / Eric D. Kolaczyk, Gábor Csárdi.

By: Contributor(s): Material type: TextTextSeries: Use R!Publisher: New York : Springer, [2014]Copyright date: ©2014Description: xiii, 228 pages : illustrations (some color) ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9783030441289 (pbk. : alk. paper)
Subject(s): DDC classification:
  • 003.015195 23
LOC classification:
  • QA402 .K6483 2014
Contents:
1. Introduction -- 2. Manipulating network data -- 3. Visualizing network data -- 4. Descriptive analysis of network graph characteristics -- 5. Mathematical models for network graphs -- 6. Statistical models for network graphs -- 7. Network topology inference -- 8. Modeling and prediction for processes on network graphs -- 9. Analysis of network flow data -- 10. Dynamic networks.
Abstract: 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).-- Source other than Library of Congress.
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Item type Current library Call number Status Date due Barcode
Book Book Indian Institute of Management Visakhapatnam 003.015195 (Browse shelf(Opens below)) Available 001833

Includes bibliographical references (225-228) and index

1. Introduction -- 2. Manipulating network data -- 3. Visualizing network data -- 4. Descriptive analysis of network graph characteristics -- 5. Mathematical models for network graphs -- 6. Statistical models for network graphs -- 7. Network topology inference -- 8. Modeling and prediction for processes on network graphs -- 9. Analysis of network flow data -- 10. Dynamic 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).-- Source other than Library of Congress.

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