The Library
Finding network communities using modularity density
Tools
Tools
Tools
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.
![[img]](https://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP_lf-201216-modden.pdf - Accepted Version - Requires a PDF viewer. Download (1014Kb) |
Official URL: http://dx.doi.org/10.1088/1742-5468/2016/12/123402
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: |
|
||||||||
| 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) |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
