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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The effort to understand network systems in increasing detail has resulted ina diversity of methods designed to extract their large-scale structure fromdata. Unfortunately, many of these methods yield diverging descriptions of thesame network, making both the comparison and understanding of their results adifficult challenge. A possible solution to this outstanding issue is to shiftthe focus away from ad hoc methods and move towards more principled approachesbased on statistical inference of generative models. As a result, we faceinstead the more well-defined task of selecting between competing generativeprocesses, which can be done under a unified probabilistic framework. Here, weconsider the comparison between a variety of generative models includingfeatures such as degree correction, where nodes with arbitrary degrees canbelong to the same group, and community overlap, where nodes are allowed tobelong to more than one group. Because such model variants possess anincreasing number of parameters, they become prone to overfitting. In thiswork, we present a method of model selection based on the minimum descriptionlength criterion and posterior odds ratios that is capable of fully accountingfor the increased degrees of freedom of the larger models, and selects the bestone according to the statistical evidence available in the data. In applyingthis method to many empirical unweighted networks from different fields, weobserve that community overlap is very often not supported by statisticalevidence and is selected as a better model only for a minority of them. On theother hand, we find that degree correction tends to be almost universallyfavored by the available data, implying that intrinsic node proprieties (asopposed to group properties) are often an essential ingredient of networkformation.

Model selection and hypothesis testing for large-scale network models with overlapping groups