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

Community detection algorithms are fundamental tools to understandorganizational principles in social networks. With the increasing power ofsocial media platforms, when detecting communities there are two possi- blesources of information one can use: the structure of social network and nodeattributes. However structure of social networks and node attributes are ofteninterpreted separately in the research of community detection. When these twosources are interpreted simultaneously, one common as- sumption shared byprevious studies is that nodes attributes are correlated with communities. Inthis paper, we present a model that is capable of combining topologyinformation and nodes attributes information with- out assuming correlation.This new model can recover communities with higher accuracy even when nodeattributes and communities are uncorre- lated. We derive the detectabilitythreshold for this model and use Belief Propagation (BP) to make inference.This algorithm is optimal in the sense that it can recover community all theway down to the threshold. This new model is also with the potential to handleedge content and dynamic settings.

Community Detection with Node Attributes and its Generalization