A novel metric for community detection

Ke Ke Shang, Michael Small, Yan Wang, Di Yin, Shu Li

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
24 Downloads (Pure)


Detection of dense communities has recently attracted increasing attention within network science and various metrics for detection of such communities have been proposed. The most popular metric - modularity - is based on the rule that the links within communities are denser than external links among communities. However, the principle of this metric suffers from ambiguity, and is based on a narrow intuition of what it means to form a "community". Instead we propose that the defining characteristic of a community is that links are more predictable within a community rather than between communities. In this letter, based on the effect of communities on link prediction, we propose a novel metric for community detection based directly on this property. We find that our metric is more robust than traditional modularity measures for each specific algorithm. Finally, we provide a measure of the improvement offered by our metric.

Original languageEnglish
Article number68002
Issue number6
Publication statusPublished - Mar 2020


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