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Statistical Analysis of Network Data

Methods and Models

  • Book
  • © 2009

Overview

Authors:
  1. Eric D. Kolaczyk
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  • Unified presentation of statistical models and methods from across the variety of disciplines engaged in ‘network science’
  • Balanced presentation of concepts and mathematics
  • Examples, including extended case studies, drawn widely from applications in the literature
  • Includes supplementary material: sn.pub/extras

Part of the book series: Springer Series in Statistics (SSS)

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Table of contents (10 chapters)

  1. Front Matter

    Pages 1-11
    Download chapter PDF

  2. Introduction and Overview

    • Eric D. Kolaczyk
    Pages 1-13

  3. Preliminaries

    • Eric D. Kolaczyk
    Pages 1-34

  4. Mapping Networks

    • Eric D. Kolaczyk
    Pages 1-30

  5. Descriptive Analysis of Network Graph Characteristics

    • Eric D. Kolaczyk
    Pages 1-44

  6. Sampling and Estimation in Network Graphs

    • Eric D. Kolaczyk
    Pages 1-30

  7. Models for Network Graphs

    • Eric D. Kolaczyk
    Pages 1-44

  8. Network Topology Inference

    • Eric D. Kolaczyk
    Pages 1-48

  9. Modeling and Prediction for Processes on Network Graphs

    • Eric D. Kolaczyk
    Pages 1-40

  10. Analysis of Network Flow Data

    • Eric D. Kolaczyk
    Pages 1-47

  11. Graphical Models

    • Eric D. Kolaczyk
    Pages 1-12

  12. Back Matter

    Pages 1-43
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Keywords

About this book

In recent years there has been an explosion of network data – that is, measu- ments that are either of or from a system conceptualized as a network – from se- ingly all corners of science. The combination of an increasingly pervasive interest in scienti c analysis at a systems level and the ever-growing capabilities for hi- throughput data collection in various elds has fueled this trend. Researchers from biology and bioinformatics to physics, from computer science to the information sciences, and from economics to sociology are more and more engaged in the c- lection and statistical analysis of data from a network-centric perspective. Accordingly, the contributions to statistical methods and modeling in this area have come from a similarly broad spectrum of areas, often independently of each other. Many books already have been written addressing network data and network problems in speci c individual disciplines. However, there is at present no single book that provides a modern treatment of a core body of knowledge for statistical analysis of network data that cuts across the various disciplines and is organized rather according to a statistical taxonomy of tasks and techniques. This book seeks to ll that gap and, as such, it aims to contribute to a growing trend in recent years to facilitate the exchange of knowledge across the pre-existing boundaries between those disciplines that play a role in what is coming to be called ‘network science.

Reviews

From the reviews:

“…Accessible and easy to read…strikes a balance between concepts and mathematical detail. …This book is a superb introduction to a fascinating area.” (International Statistical Review, 2010, 78, 1, 134-159) “Many disciplines are nowadays involved in network modeling, but it appears as if a common methodological foundation is lacking. The objective of this book is to provide a first attempt at defining such a common methodological foundation from a statistical point of view. … The style of the writing is excellent. … ample references allow quick access to further literature. I can recommend this book to anyone with a serious statistical interest in networks.” (Fred van Eeuwijk, VOC Nieuwsbrief, Issue 44, May, 2010)

“Any reader interested in networks and wanting a perspective beyond that of any single discipline should acquire this book. … Researchers will also appreciate the many points in the book where important open problems are identified. The book can also serve readily and flexibly as the main textbook for either a graduate-level seminar course or for an informally organized reading group. … This book sets itself the challenge of addressing statistics for network science broadly, and in the many ways already noted, it is successful.” (Michael Frey, Technometrics, Vol. 54 (1), February, 2012)

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