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

Temporal network data often encode time-stamped interaction events betweensenders and receivers, such as co-authoring a scientific article or sending anemail. A number of relational event frameworks have been proposed to addressspecific issues raised by modelling time-stamped data with complex temporal andspatial dependencies. These models attempt to quantify how individuals'behaviour, external factors and interaction with other individuals change thenetwork structure over time. It is often of interest to determine whetherchanges in the network can be attributed to endogenous mechanisms reflectingnatural relational tendencies, such as reciprocity or triadic effects, with thelatter thought to represent the inherent complexity of human interaction.  The propensity to form (or receive) ties can also be related to individualactors' attributes. Nodal heterogeneity in the network is often modelled byincluding actor-specific or dyadic covariates, such as age, gender, sharedneighbourhood, etc. However, capturing personality traits such as popularity orexpansiveness is difficult, if not impossible. A failure to account forunobserved heterogeneity may confound the substantive effect of key variablesof interest. This research shows how node level popularity in terms of senderand receiver effects may mask ghost triadic effects. These results suggest thatunobserved nodal heterogeneity plays a substantial role in REM estimationprocedure and influences the conclusions drawn from real-world networks.

Nodal heterogeneity may induce ghost triadic effects in relational event models