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

Modularity is a widely used measure for evaluating community structure innetworks. The definition of modularity involves a comparison ofwithin-community edges in the observed network and that number in an equivalentrandomized network. This equivalent randomized network is called the nullmodel, which serves as a reference. To make the comparison significant, thenull model should characterize some features of the observed network. However,the null model in the original definition of modularity is unrealisticallymixed, in the sense that any node can be linked to any other node withoutpreference and only connectivity matters. Thus, it fails to be a goodrepresentation of real-world networks. A common feature of many real-worldnetworks is "similarity attraction", i.e., edges tend to link to nodes that aresimilar to each other. We propose a null model that captures the similarityattraction feature. This null model enables us to create a framework fordefining a family of Dist-Modularity adapted to various networks, includingnetworks with additional information on nodes. We demonstrate thatDist-Modularity is useful in identifying communities at different scales.

Extending modularity by capturing the similarity attraction feature in the null model