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 the problem of link prediction, based on partial observation of alarge network, and on side information associated to its vertices. Thegenerative model is formulated as a matrix logistic regression. The performanceof the model is analysed in a high-dimensional regime under a structuralassumption. The minimax rate for the Frobenius-norm risk is established and acombinatorial estimator based on the penalised maximum likelihood approach isshown to achieve it. Furthermore, it is shown that this rate cannot be attainedby any (randomised) algorithm computable in polynomial time under acomputational complexity assumption.

Optimal link prediction with matrix logistic regression