Exponential Family Random Graph Models: A data-driven bridge between networks and epidemics
Seminar Room 1, Newton Institute
AbstractCo-authors: Mark S.Handcock (University of California Los Angeles), David R. Hunter (Pennsylvania State University), Carter T. Butts (University of California Irvine), Steven M. Goodreau (University of Washington), Skye Bender-deMoll (At Large), Pavel Krivitsky (University of Woolongong)
In a small comment on the Mollison, Isham and Grenfell JRSS paper at the end of the Newton Workshop in 1994, I speculated on the potential for an emerging stochastic modeling framework to provide the missing link between network and epidemic modeling. Now, 30 years later, that link is firmly established. In this talk I will briefly summarize the theory of Exponential Family Random Graph Models (ERGMs), a comprehensive statistical framework that makes it possible to estimate generative parameters for network structure from a wide range of data, and simulate static or dynamic networks with the observed features. The talk will cover the extensive software available in the "statnet" related packages on CRAN and highlight some recent applications to epidemic modeling.
Related Links: •https://statnet.csde.washington.edu/trac - the statnet wiki •http://www.jstatsoft.org/v24/ - Journal of Statistical Software Volume on statnet (2008) •http://statnet.csde.washington.edu/movies/ - A network epidemiology movie
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