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

Two dimensional matrices with binary (0/1) entries are a common datastructure in many research fields. Examples include ecology, economics,mathematics, physics, psychometrics and others. Because the columns and rows ofthese matrices represent distinct entities, they can equivalently be expressedas a pair of bipartite networks that are linked by projection. A variety ofdiversity statistics and network metrics can then be used to quantify patternsin these matrices and networks. But what should these patterns be compared to?In all of these disciplines, researchers have recognized the necessity ofcomparing an empirical matrix to a benchmark set of "null" matrices created byrandomizing certain elements of the original data. This common need hasnevertheless promoted the independent development of methodologies byresearchers who come from different backgrounds and use different terminology.Here, we provide a multidisciplinary review of randomization techniques formatrices representing binary, bipartite networks. We aim to translate theconcepts from different technical domains into a common language that isaccessible to a broad scientific audience. Specifically, after brieflyreviewing examples of binary matrix structures across different fields, weintroduce the major approaches and common strategies for randomizing thesematrices. We then explore the details of and performance of specifictechniques, and discuss their limitations and computational challenges. Inparticular, we focus on the conceptual importance and implementation ofstructural constraints on the randomization, such as preserving row or columnssums of the original matrix in each of the randomized matrices. Our reviewserves both as a guide for empiricists in different disciplines, as well as areference point for researchers working on theoretical and methodologicaldevelopments in matrix randomization methods.

Pattern detection in bipartite networks: a review of terminology, applications and methods