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

As the availability and importance of temporal interaction data--such asemail communication--increases, it becomes increasingly important to understandthe underlying structure that underpins these interactions. Often theseinteractions form a multigraph, where we might have multiple interactionsbetween two entities. Such multigraphs tend to be sparse yet structured, andtheir distribution often evolves over time. Existing statistical models withinterpretable parameters can capture some, but not all, of these properties. Wepropose a dynamic nonparametric model for interaction multigraphs that combinesthe sparsity of edge-exchangeable multigraphs with dynamic clustering patternsthat tend to reinforce recent behavioral patterns. We show that our methodyields improved held-out likelihood over stationary variants, and impressivepredictive performance against a range of state-of-the-art dynamic graphmodels.

A Nonparametric Bayesian Model for Sparse Dynamic Multigraphs