We have hosted the application community detection modularity suite in order to run this application in our online workstations with Wine or directly.


Quick description about community detection modularity suite:

- MixtureModel_v1r1: overlapping community algorithm [3], which includes novel partition density and fuzzy modularity metrics.

- OpenMP versions of algorithms in [1] are available to download.

- Main suite containing three community detection algorithms based on the Modularity measure containing: Geodesic and Random Walk edge Betweenness [1] and Spectral Modularity [2].


Collaborator: Theologos Kotsos.

[1] M. Newman & M. Girvan, Physical Review, E 69 (026113), 2004.
[2] M. Newman, Physical Review E, 74(3): 036104, 2006.
[3] B. Ball et al, An efficient and principled method for detecting communities in networks, 2011.
The suite is based upon the fast community algorithm implemented by Aaron Clauset <[email protected]>, Chris Moore, Mark Newman, and the R IGraph library Copyright (C) 2007 Gabor Csardi <[email protected]>. It also makes of the classes available from Numerical Recipies 3rd Edition W. Press, S. Teukolsky, W. Vetterling, B. Flanne.

Features:
  • C++ and R implementations of Newman & Girvan Modularity based community detection algorithms
  • R implementations of the edge Betweenness Random Walk algorithm
  • Boot-strapping factilities to test cluster robustness
  • Version 2 (v2r1) contains OpenMP implementation of Newman & Girvan Geodesic and Random Walk edge Betweenness algorithms
  • Overlapping community detection model, including the partition density and fuzzy modularity metrics for community detection


Audience: Science/Research.
User interface: Console/Terminal.
Programming Language: C++, S/R.
Categories:
Bio-Informatics, Machine Learning, Statistics

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