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

Full probability models are critical for the statistical modeling of complexnetworks, and yet there are few general, flexible and widely applicablegenerative methods. We propose a new family of probability models motivated bythe idea of network growth, which we call the Latent Order Logistic (LOLOG)model. LOLOG is a fully general framework capable of describing any probabilitydistribution over graph configurations, though not all distributions are easilyexpressible or estimable as a LOLOG. We develop inferential procedures based onMonte Carlo Method of Moments, Generalized Method of Moments and variationalinference. To show the flexibility of the model framework, we show howso-called scale-free networks can be modeled as LOLOGs via preferentialattachment. The advantages of LOLOG in terms of avoidance of degeneracy, easeof sampling, and model flexibility are illustrated. Connections with thepopular Exponential-family Random Graph model (ERGM) are also explored, and wefind that they are identical in the case of dyadic independence. Finally, weapply the model to a social network of collaboration within a corporate lawfirm, a friendship network among adolescent students, and the friendshiprelations in an online social network.

A New Generative Statistical Model for Graphs: The Latent Order Logistic (LOLOG) Model