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Finding network communities using modularity density

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Botta, Federico and Del Genio, Charo I. (2016) Finding network communities using modularity density. Journal of Statistical Mechanics : Theory and Experiment, 2016 (12). 123402. doi:10.1088/1742-5468/2016/12/123402 ISSN 1742-5468.

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Official URL: http://dx.doi.org/10.1088/1742-5468/2016/12/123402

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Abstract

Many real-world complex networks exhibit a community structure, in which the modules correspond to actual functional units. Identifying these communities is a key challenge for scientists. A common approach is to search for the network partition that maximizes a quality function. Here, we present a detailed analysis of a recently proposed function, namely modularity density. We show that it does not incur in the drawbacks suffered by traditional modularity, and that it can identify networks without ground-truth community structure, deriving its analytical dependence on link density in generic random graphs. In addition, we show that modularity density allows an easy comparison between networks of different sizes, and we also present some limitations that methods based on modularity density may suffer from. Finally, we introduce an efficient, quadratic community detection algorithm based on modularity density maximization, validating its accuracy against theoretical predictions and on a set of benchmark networks.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Research Centres > Centre for Complexity Science
Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- )
Library of Congress Subject Headings (LCSH): Modules (Algebra), Computational complexity -- Mathematical models, Algorithms
Journal or Publication Title: Journal of Statistical Mechanics : Theory and Experiment
Publisher: Institute of Physics Publishing Ltd.
ISSN: 1742-5468
Official Date: 19 December 2016
Dates:
DateEvent
19 December 2016Available
27 October 2016Accepted
4 May 2016Submitted
Volume: 2016
Number: 12
Article Number: 123402
DOI: 10.1088/1742-5468/2016/12/123402
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 21 December 2016
Date of first compliant Open Access: 19 November 2017
Funder: Engineering and Physical Sciences Research Council (EPSRC), Seventh Framework Programme (European Commission) (FP7)
Grant number: EP/E501311/1 (EPSRC), 288021 (FP7)

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