Amazon cover image
Image from Amazon.com

A survey of statistical network models [electronic resource] / Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg and Edoardo M. Airoldi.

Contributor(s): Material type: TextTextSeries: Foundations and trends in machine learning (Online) ; v. 2, issue 2, p. 129-233.Publication details: Hanover, Mass. : Now Publishers, c2010.Description: 1 electronic text (p. [129]-233 : ill. (some col.)) : digital fileISBN:
  • 9781601983213 (electronic)
Other title:
  • Statistical network models
Subject(s): DDC classification:
  • 004.6 22
LOC classification:
  • TK5105.5 .S877 2010
  • QA402 .S877 2010
Online resources: Available additional physical forms:
  • Also available in print.
Contents:
1. Introduction -- 2. Motivation and data-set examples -- 3. Static network models -- 4. Dynamic models for longitudinal data -- 5. Issues in network modeling -- 6. Summary -- Acknowledgments -- References.
Summary: Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active "network community" and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online "networking communities" such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library EBook Available
Total holds: 0

Title from PDF (viewed on March 2, 2010).

Includes bibliographical references (p. 212-233).

1. Introduction -- 2. Motivation and data-set examples -- 3. Static network models -- 4. Dynamic models for longitudinal data -- 5. Issues in network modeling -- 6. Summary -- Acknowledgments -- References.

Restricted to subscribers or individual document purchasers.

Google Scholar

Google Book Search

INSPEC

Scopus

ACM Computing Guide

DBPLP Computer Science Bibliography

Zentralblatt MATH Database

AMS MathSciNet

ACM Computing Reviews

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active "network community" and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online "networking communities" such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.

Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg and Edoardo M. Airoldi (2009) "A Survey of Statistical Network Models", Foundations and Trends in Machine Learning: Vol. 2: No 2, pp 129-233.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Powered by Koha