A Perfect Sampling Method for Exponential Family Random Graph Models

Abstract

Generation of deviates from random graph models with non-trivial edge dependence is an increasingly important problem. Here, we introduce a method which allows perfect sampling from random graph models in exponential family form ("exponential family random graph" models), using a variant of Coupling From The Past. We illustrate the use of the method via an application to the Markov graphs, a family that has been the subject of considerable research. We also show how the method can be applied to a variant of the biased net models, which are not exponentially parameterized.

To appear in the Journal of Mathematical Sociology (accepted version)

Cited by in corpus (4)

• Exponential-Family Models of Random Graphs: Inference in Finite-, Super-, and Infinite Population Scenarios
• Continuous Time Graph Processes with Known ERGM Equilibria: Contextual Review, Extensions, and Synthesis
• Simulating Markov random fields with a conclique-based Gibbs sampler
• A Return to Biased Nets: New Specifications and Approximate Bayesian Inference