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Incorporating Covariates Into Stochastic Blockmodels

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  • Tracy M. Sweet

    (University of Maryland)

Abstract

Social networks in education commonly involve some form of grouping, such as friendship cliques or teacher departments, and blockmodels are a type of statistical social network model that accommodate these grouping or blocks by assuming different within-group tie probabilities than between-group tie probabilities. We describe a class of models, covariate stochastic blockmodels (CSBMs), that incorporates covariates into blockmodels. These models not only estimate the effects of covariates in the presence of the block structure but also can determine differential covariate effects such as within blocks versus between blocks. For example, education researchers can now determine those factors that mitigate relationships both within schools and between schools. We introduce several CSBMs as examples and present a series of simulation studies to investigate both the feasibility and some operating characteristics as well as fit CSBMs to real network data.

Suggested Citation

  • Tracy M. Sweet, 2015. "Incorporating Covariates Into Stochastic Blockmodels," Journal of Educational and Behavioral Statistics, , vol. 40(6), pages 635-664, December.
    • Handle: RePEc:sae:jedbes:v:40:y:2015:i:6:p:635-664
    DOI: 10.3102/1076998615606110
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    1. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    2. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    3. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    4. Nowicki K. & Snijders T. A. B., 2001. "Estimation and Prediction for Stochastic Blockstructures," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1077-1087, September.
    5. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
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