Computational statistical methods for social network models

David R. Hunter, Pavel N. Krivitsky, Michael Schweinberger

Research output: Contribution to journalArticle

32 Scopus citations

Abstract

We review the broad range of recent statistical work in social network models, with emphasis on computational aspects of these methods. Particular focus is applied to exponential-family random graph models (ERGM) and latent variable models for data on complete networks observed at a single time point, though we also briefly review many methods for incompletely observed networks and networks observed at multiple time points. Although we mention far more modeling techniques than we can possibly cover in depth, we provide numerous citations to current literature. We illustrate several of the methods on a small, well-known network dataset, Sampson's monks, providing code where possible so that these analyses may be duplicated.

Original languageEnglish (US)
Pages (from-to)856-882
Number of pages27
JournalJournal of Computational and Graphical Statistics
Volume21
Issue number4
DOIs
StatePublished - 2012

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All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

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