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Open Access Publications from the University of California

Bayesian modeling and uncertainty quantificationfor descriptive social networks

  • Author(s): Nemmers, Thomas
  • Narayan, Anjana
  • Banerjee, Sudipto
  • et al.
The data associated with this publication are in the supplemental files.

This article presents a simple and easily implementableBayesian approach to model and quantify uncertainty insmall descriptive social networks. While statistical methodsfor analyzing networks have seen burgeoning activity overthe last decade or so, ranging from social sciences to genetics,such methods usually involve sophisticated stochasticmodels whose estimation requires substantial structure andinformation in the networks. At the other end of the analyticspectrum, there are purely descriptive methods based uponquantities and axioms in computational graph theory. In socialnetworks, popular descriptive measures include, but arenot limited to, the so called Krackhardt’s axioms. Anotherapproach, recently gaining attention, is the use of PageRankalgorithms. While these descriptive approaches provide insightinto networks with limited information, including smallnetworks, there is, as yet, little research detailing a statisticalapproach for small networks. This article aims to contributeat the interface of Bayesian statistical inference andsocial network analysis by offering practicing social scientistsa relatively straightforward Bayesian approach to accountfor uncertainty while conducting descriptive social networkanalysis. The emphasis is on computational feasibility andeasy implementation using existing R packages, such as snaand rjags, that are available from the Comprehensive RArchive Network ( We analyzea network comprising 18 websites from the US and UK todiscern transnational identities, previously analyzed usingdescriptive graph theory with no uncertainty quantification,using fully Bayesian model-based inference.

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