Work

Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects

Year: 2008

Type: article

Abstract: Exponential-family random graph models (ERGMs) represent the processes that govern the formation of links in networks through the terms selected by the user. The terms specify network statistics that are sufficient to represent the probability distribution over the space of networks of that size. Many classes of statistics can be used. In this article we describe the classes of statistics that are currently available in the ergm package. We also describe means for controlling the Markov chain Monte Carlo (MCMC) algorithm that the package uses for estimation. These controls a ect either the proposal distribution on the sample space used by the underlying Metropolis-Hastings algorithm or the constraints on the sample space itself. Finally, we describe various other arguments to core functions of the ergm package.

Source: Journal of Statistical Software

DOI: 10.18637/jss.v024.i04

Authors: Martina Morris, Mark S. Handcock, David R. Hunter

Institution: University of Washington

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: diamond

Funders: National Institutes of Health, National Institute on Drug Abuse, National Institute of General Medical Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development

Awards: G1431855629, Quantifying HIV Transmission Risk in Sex/Drug Networks, Modeling HIV and STD in Drug User and Social Networks, Center for Studies in Demography and Ecology, Novel Statistical Models for Synthesizing Social Networks and Epidemic Dynamics