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Exponential Random Graph Models for Social Networks : Theory, Methods, and Applications / edited by Dean Lusher, Johan Koskinen, Garry Robins.

Contributor(s): Material type: TextTextSeries: Structural Analysis in the Social Sciences ; 35 | Structural Analysis in the Social Sciences ; 35.Publisher: Cambridge : Cambridge University Press, 2012Description: 1 online resource (360 pages) : digital, PDF file(s)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780511894701 (ebook)
Additional physical formats: Print version: : No titleDDC classification:
  • 302.3 23
LOC classification:
  • HM741 .E96 2013
Online resources: Summary: Exponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure and network processes. The chapters in this edited volume provide a self-contained, exhaustive account of the theoretical and methodological underpinnings of ERGMs, including models for univariate, multivariate, bipartite, longitudinal and social-influence type ERGMs. Each method is applied in individual case studies illustrating how social science theories may be examined empirically using ERGMs. The authors supply the reader with sufficient detail to specify ERGMs, fit them to data with any of the available software packages and interpret the results.
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Title from publisher's bibliographic system (viewed on 04 Apr 2016).

Exponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure and network processes. The chapters in this edited volume provide a self-contained, exhaustive account of the theoretical and methodological underpinnings of ERGMs, including models for univariate, multivariate, bipartite, longitudinal and social-influence type ERGMs. Each method is applied in individual case studies illustrating how social science theories may be examined empirically using ERGMs. The authors supply the reader with sufficient detail to specify ERGMs, fit them to data with any of the available software packages and interpret the results.

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