DyCoNet: a Gephi plugin for community detection in dynamic complex networks

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 dynami...

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Bibliographic Details
Published in:PloS one 2014-07, Vol.9 (7), p.e101357-e101357
Main Authors: Kauffman, Julie, Kittas, Aristotelis, Bennett, Laura, Tsoka, Sophia
Format: Article
Language:English
Subjects:
Algorithms
Architectural engineering
Bioinformatics
Biology and Life Sciences
Communities
Community structure
Computer and Information Sciences
Computer Graphics
Gene expression
Informatics
Methods
Models, Theoretical
Networks
Physics
Protein Interaction Maps
Proteins
Social networks
Social organization
Social Sciences
Software
Source code
Visualization
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0101357