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

Understanding the information behind social relationships represented by anetwork is very challenging, especially, when the social interactions changeover time inducing updates on the network topology. In this context, this paperproposes an approach for analysing dynamic social networks, more precisely forTwitter's network. Our approach relies on two complementary steps: (i) anonline community identification based on a dynamic community detectionalgorithm called Dyci. The main idea of Dyci is to track whether a connectedcomponent of the weighted graph becomes weak over time, in order to merge itwith the "dominant" neighbour community. Additionally, (ii) a communityvisualization is provided by our visualization tool called NLCOMS, whichcombines between two methods of dynamic network visualization. In order toassess the efficiency and the applicability of the proposed approach, weconsider real-world data of the ANR-Info-RSN project, which deals withcommunity analysis in Twitter.

On the visualization of the detected communities in dynamic networks: A case study of Twitter's network