Statistical Models for Social Networks

Tom A. B. Snijders*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

207 Citations (Scopus)

Abstract

Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For single networks, the older literature concentrated on conditionally uniform models.. Various types of latent space models have been developed: for discrete, general metric, ultrametric, Euclidean, and partially ordered spaces. Exponential random graph models were proposed long ago but now are applied more and more thanks to the non-Markovian social circuit specifications that were recently proposed. Modeling network dynamics is less complicated than modeling single network observations because dependencies are spread out in time. For modeling network dynamics, continuous-time models are more fruitful. Actor-oriented models here provide a model that can represent many dependencies in a flexible way. Strong model development is now going on to combine the features of these models and to extend them to more complicated outcome spaces.

Original languageEnglish
Title of host publicationANNUAL REVIEW OF SOCIOLOGY, VOL 37
EditorsKS Cook, DS Massey
Place of PublicationPALO ALTO
PublisherAnnual Reviews
Pages131-153
Number of pages23
ISBN (Print)978-0-8243-2237-3
DOIs
Publication statusPublished - 2011

Publication series

NameAnnual Review of Sociology
PublisherANNUAL REVIEWS
Volume37
ISSN (Print)0360-0572

Keywords

  • social networks
  • statistical modeling
  • inference
  • RANDOM GRAPH MODELS
  • P-ASTERISK MODELS
  • EXPONENTIAL FAMILY MODELS
  • STOCHASTIC BLOCKMODELS
  • DIRECTED-GRAPHS
  • MARKOV GRAPHS
  • DYADIC DATA
  • PREDICTION
  • INFERENCE
  • DYNAMICS

Cite this