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PLoS One. 2011;6(9):e24195. doi: 10.1371/journal.pone.0024195. Epub 2011 Sep 1.

Deciphering network community structure by surprise.

Author information

  • 1Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas, Valencia, Spain.

Abstract

The analysis of complex networks permeates all sciences, from biology to sociology. A fundamental, unsolved problem is how to characterize the community structure of a network. Here, using both standard and novel benchmarks, we show that maximization of a simple global parameter, which we call Surprise (S), leads to a very efficient characterization of the community structure of complex synthetic networks. Particularly, S qualitatively outperforms the most commonly used criterion to define communities, Newman and Girvan's modularity (Q). Applying S maximization to real networks often provides natural, well-supported partitions, but also sometimes counterintuitive solutions that expose the limitations of our previous knowledge. These results indicate that it is possible to define an effective global criterion for community structure and open new routes for the understanding of complex networks.

PMID:
21909420
[PubMed - indexed for MEDLINE]
PMCID:
PMC3164713
Free PMC Article

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