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

This paper presents a novel spectral algorithm with additive clusteringdesigned to identify overlapping communities in networks. The algorithm isbased on geometric properties of the spectrum of the expected adjacency matrixin a random graph model that we call stochastic blockmodel with overlap (SBMO).An adaptive version of the algorithm, that does not require the knowledge ofthe number of hidden communities, is proved to be consistent under the SBMOwhen the degrees in the graph are (slightly more than) logarithmic. Thealgorithm is shown to perform well on simulated data and on real-world graphswith known overlapping communities.

A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks