hergm: Hierarchical Exponential-Family Random Graph Models
- Resource Type
- article
- Authors
- Michael Schweinberger; Pamela Luna
- Source
- Journal of Statistical Software, Vol 85, Iss 1, Pp 1-39 (2018)
- Subject
- social networks
random graphs
Markov random graph models
exponential-family random graph models
stochastic block models
model-based clustering
Statistics
HA1-4737
- Language
- English
- ISSN
- 1548-7660
We describe the R package hergm that implements hierarchical exponential-family random graph models with local dependence. Hierarchical exponential-family random graph models with local dependence tend to be superior to conventional exponential-family random graph models with global dependence in terms of goodness-of-fit. The advantage of hierarchical exponential-family random graph models is rooted in the local dependence induced by them. We discuss the notion of local dependence and the construction of models with local dependence along with model estimation, goodness-of-fit, and simulation. Simulation results and three applications are presented.