English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We consider the challenging problem of statistical inference forexponential-family random graph models based on a single observation of arandom graph with complex dependence. To facilitate statistical inference, weconsider random graphs with additional structure in the form of blockstructure. We have shown elsewhere that when the block structure is known, itfacilitates consistency results for $M$-estimators of canonical and curvedexponential-family random graph models with complex dependence, such astransitivity. In practice, the block structure is known in some applications(e.g., multilevel networks), but is unknown in others. When the block structureis unknown, the first and foremost question is whether it can be recovered withhigh probability based on a single observation of a random graph with complexdependence. The main consistency results of the paper show that it is possibleto do so under weak dependence and smoothness conditions. These results confirmthat exponential-family random graph models with block structure constitute apromising direction of statistical network analysis.

Consistent structure estimation of exponential-family random graph models with block structure