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

Most real-world networks evolve over time. Existing literature proposesmodels for dynamic networks that are either unlabeled or assumed to have asingle membership structure. On the other hand, a new family of MixedMembership Stochastic Block Models (MMSBM) allows to model static labelednetworks under the assumption of mixed-membership clustering. In this work, wepropose to extend this later class of models to infer dynamic labeled networksunder a mixed membership assumption. Our approach takes the form of a temporalprior on the model's parameters. It relies on the single assumption thatdynamics are not abrupt. We show that our method significantly differs fromexisting approaches, and allows to model more complex systems --dynamic labelednetworks. We demonstrate the robustness of our method with several experimentson both synthetic and real-world datasets. A key interest of our approach isthat it needs very few training data to yield good results. The performancegain under challenging conditions broadens the variety of possible applicationsof automated learning tools --as in social sciences, which comprise many fieldswhere small datasets are a major obstacle to the introduction of machinelearning methods.

Dynamic Mixed Membership Stochastic Block Model for Weighted Labeled Networks