A Perfect Sampling Method for Exponential Family Random Graph Models
Computation
2020-01-07 v2 Social and Information Networks
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.
Cite
@article{arxiv.1710.02786,
title = {A Perfect Sampling Method for Exponential Family Random Graph Models},
author = {Carter T. Butts},
journal= {arXiv preprint arXiv:1710.02786},
year = {2020}
}
Comments
To appear in the Journal of Mathematical Sociology (accepted version)