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 structure is of paramount importance for the understanding ofcomplex networks. Consequently, there is a tremendous effort in order todevelop efficient community detection algorithms. Unfortunately, the issue of afair assessment of these algorithms is a thriving open question. If theground-truth community structure is available, various clustering-based metricsare used in order to compare it versus the one discovered by these algorithms.However, these metrics defined at the node level are fairly insensitive to thevariation of the overall community structure. To overcome these limitations, wepropose to exploit the topological features of the 'community graphs' (wherethe nodes are the communities and the links represent their interactions) inorder to evaluate the algorithms. To illustrate our methodology, we conduct acomprehensive analysis of overlapping community detection algorithms using aset of real-world networks with known a priori community structure. Resultsprovide a better perception of their relative performance as compared toclassical metrics. Moreover, they show that more emphasis should be put on thetopology of the community structure. We also investigate the relationshipbetween the topological properties of the community structure and thealternative evaluation measures (quality metrics and clustering metrics). Itappears clearly that they present different views of the community structureand that they must be combined in order to evaluate the effectiveness ofcommunity detection algorithms.

Community detection algorithm evaluation with ground-truth data