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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Directed networks are conveniently represented as graphs in which orderededges encode interactions between vertices. Despite their wide availability,there is a shortage of statistical models amenable for inference, speciallywhen contextual information and degree heterogeneity are present. This paperpresents an annotated graph model with parameters explicitly accounting forthese features. To overcome the curse of dimensionality due to modelling degreeheterogeneity, we introduce a sparsity assumption and propose a penalizedlikelihood approach with $\ell_1$-regularization for parameter estimation. Westudy the estimation and selection consistency of this approach under a sparsenetwork assumption, and show that inference on the covariate parameter isstraightforward, thus bypassing the need for the kind of debiasing commonlyemployed in $\ell_1$-penalized likelihood estimation. Simulation and dataanalysis corroborate our theoretical findings.

An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks