DyCoNet: A Gephi Plugin for Community Detection in Dynamic Complex Networks

Julie Kauffman, Aristotelis Kittas, Laura Bennett, Sophia Tsoka*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)
76 Downloads (Pure)

Abstract

Community structure detection has proven to be important in revealing the underlying organisation of complex networks. While most current analyses focus on static networks, the detection of communities in dynamic data is both challenging and timely. An analysis and visualisation procedure for dynamic networks is presented here, which identifies communities and sub-communities that persist across multiple network snapshots. An existing method for community detection in dynamic networks is adapted, extended, and implemented. We demonstrate the applicability of this method to detect communities in networks where individuals tend not to change their community affiliation very frequently. When stability of communities cannot be assumed, we show that the sub-community model may be a better alternative. This is illustrated through test cases of social and biological networks. A plugin for Gephi, an open-source software program used for graph visualisation and manipulation, named "DyCoNet'', was created to execute the algorithm and is freely available from https://github.com/juliemkauffman/DyCoNet.

Original languageEnglish
Article numbere101357
Number of pages8
JournalPL o S One
Volume9
Issue number7
Early online date7 Jul 2014
DOIs
Publication statusPublished - 2014

Keywords

  • PROTEIN-INTERACTION NETWORKS
  • FUNCTIONAL MODULES
  • TIME-COURSE

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