title: A Novel Simulation Method for Binary Discrete Exponential Families, with Application to Social Networks.
abstract: Stochastic models for finite binary vectors are widely used in sociology, with examples ranging from social influence models on dichotomous behaviors or attitudes to models for random graphs. Exact sampling for such models is difficult in the presence of dependence, leading to the use of Markov chain Monte Carlo (MCMC) as an approximation technique. While often effective, MCMC methods have variable execution time, and the quality of the resulting draws can be difficult to assess. Here, we present a novel alternative method for approximate sampling from binary discrete exponential families having fixed execution time and well-defined quality guarantees. We demonstrate the use of this sampling procedure in the context of random graph generation, with an application to the simulation of a large-scale social network using both geographical covariates and dyadic dependence mechanisms.
authors.affiliation: Departments of Sociology, Statistics, and EECS, and Institute for Mathematical Behavioral Sciences; University of California, Irvine; SSPA 2145; Irvine, CA 92697-5100; buttsc@uci.edu.
authors.forename: Carter T
authors.initials: CT
authors.lastname: Butts
keywords: discrete exponential families; random graphs; statistical simulation