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

Dynamic network analysis has found an increasing interest in the literaturebecause of the importance of different kinds of dynamic social networks,biological networks, and economic networks. Most available probability andstatistical models for dynamic network data are deduced from random graphtheory where the networks are characterized on the node and edge level. Theyare often very restrictive for applications and unscalable to high-dimensionaldynamic network data which is very common nowadays. In this paper, we take adifferent perspective: The evolving sequence of networks are treated as a timeseries of network matrices. We adopt a matrix factor model where the observedsurface dynamic network is assumed to be driven by a latent dynamic networkwith lower dimensions. The linear relationship between the surface network andthe latent network is characterized by unknown but deterministic loadingmatrices. The latent network and the corresponding loadings are estimated viaan eigenanalysis of a positive definite matrix constructed from theauto-cross-covariances of the network times series, thus capturing the dynamicspresenting in the network. The proposed method is able to unveil the latentdynamic structure and achieve the objective of dimension reduction. Differentfrom other dynamic network analytical methods that build on latent variables,our approach imposes neither any distributional assumptions on the underlyingnetwork nor any parametric forms on its covariance function. The latent networkis learned directly from the data with little subjective input. We applied theproposed method to the monthly international trade flow data from 1981 to 2015.The results unveil an interesting evolution of the latent trading network andthe relations between the latent entities and the countries.

Factor Models for High-Dimensional Dynamic Networks: with Application to International Trade Flow Time Series 1981-2015