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

We consider a stochastic blockmodel equipped with node covariate information,that is helpful in analyzing social network data. The key objective is toobtain maximum likelihood estimates of the model parameters. For this task, wedevise a fast, scalable Monte Carlo EM type algorithm based on case-controlapproximation of the log-likelihood coupled with a subsampling approach. A keyfeature of the proposed algorithm is its parallelizability, by processingportions of the data on several cores, while leveraging communication of keystatistics across the cores during each iteration of the algorithm. Theperformance of the algorithm is evaluated on synthetic data sets and comparedwith competing methods for blockmodel parameter estimation. We also illustratethe model on data from a Facebook derived social network enhanced with nodecovariate information.

Likelihood Inference for Large Scale Stochastic Blockmodels with  Covariates based on a Divide-and-Conquer Parallelizable Algorithm with  Communication