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

The configuration model is a standard tool for uniformly generating randomgraphs with a specified degree sequence, and is often used as a null model toevaluate how much of an observed network's structure can be explained by itsdegree structure alone. A Markov chain Monte Carlo (MCMC) algorithm, based on adegree-preserving double-edge swap, provides an asymptotic solution to samplefrom the configuration model. However, accurately and efficiently detectingthis Markov chain's convergence on its stationary distribution remains anunsolved problem. Here, we provide a solution to detect convergence and samplefrom the configuration model. We develop an algorithm, based on theassortativity of the sampled graphs, for estimating the gap between effectivelyindependent MCMC states, and a computationally efficient gap-estimationheuristic derived from analyzing a corpus of 509 empirical networks. We providea convergence detection method based on the Dickey-Fuller Generalized LeastSquares test, which we show is more accurate and efficient than threealternative Markov chain convergence tests.

Sampling random graphs with specified degree sequences