TY - JOUR
T1 - Computational statistical methods for social network models
AU - Hunter, David R.
AU - Krivitsky, Pavel N.
AU - Schweinberger, Michael
N1 - Funding Information:
The authors gratefully acknowledge the support by the Office of Naval Research under the MURI program, Award Number N00014-08-1-1015, and the National Institutes of Health, Award Numbers R01 HD068395 and R01 GM083603.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
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U2 - 10.1080/10618600.2012.732921
DO - 10.1080/10618600.2012.732921
M3 - Article
AN - SCOPUS:84898866090
VL - 21
SP - 856
EP - 882
JO - Journal of Computational and Graphical Statistics
JF - Journal of Computational and Graphical Statistics
SN - 1061-8600
IS - 4
ER -