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Dynamic elicited priors for updating covert networks

Published online by Cambridge University Press:  15 April 2013

JEFF GILL and
JOHN R. FREEMAN
JEFF GILL
Affiliation:
Department of Political Science, Department of Biostatistics, and Department of Surgery (Public Health Sciences), Washington University, One Brookings Drive, Seigle Hall, St. Louis, MO 63130, USA (e-mail: jgill@wustl.edu)
JOHN R. FREEMAN
Affiliation:
Department of Political Science, University of Minnesota, 1414 Social Sciences Bldg, 267 19th Avenue South, Minneapolis, MN 55455, USA
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Abstract

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The study of covert networks is plagued by the fact that individuals conceal their attributes and associations. To address this problem, we develop a technology for eliciting this information from qualitative subject-matter experts to inform statistical social network analysis. We show how the information from the subjective probability distributions can be used as input to Bayesian hierarchical models for network data. In the spirit of “proof of concept,” the results of a test of the technology are reported. Our findings show that human subjects can use the elicitation tool effectively, supplying attribute and edge information to update a network indicative of a covert one.

Keywords

covert networksBayesian elicitationlink prediction

Information

Type
Research Article
Information
Network Science , Volume 1 , Issue 1 , April 2013, pp. 68 - 94
Copyright
Copyright © Cambridge University Press 2013

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