Thanks Minh,
The papers look interesting. Has anyone implemented any of the methods
into igraph (or any other library for that matter)? I'm a microbial
ecologist, not a mathematician or a computer scientist. I like to use
statistical methods but haven't got the knowledge, time or inclination
to write complex algorithms.
Chen -- do you mean edge.betweenness.community()?
I tried it but then R hangs (haven't tried letting run for a few hours
though).
I appreciate your help,
Roey
On Wed 26 Sep 2012 12:54:11 PM CEST, 凌琛 wrote:
Hi,
Have you used the Weighted Girvan-Newman community detected algorithm
in the library to detect community?
I am not sure what the edge weight represent in this algorithm in
Igraph, similarity or distance?
Thanks,
chen
On Wed, Sep 26, 2012 at 6:48 PM, Minh Nguyen <[email protected]
<mailto:[email protected]>> wrote:
Hi,
On Wed, Sep 26, 2012 at 8:21 PM, Roey Angel
<[email protected] <mailto:[email protected]>> wrote:
> I was wondering if there's a way to choose between the methods,
either by
> experience (e.g. 'method x typically gives best results) or with
some
> statistical test.
There is a general class of statistical tests called the significance
of community. The relevant ideas and techniques can be found in the
following papers.
http://dx.doi.org/10.1371/journal.pone.0033721
http://dx.doi.org/10.1103/PhysRevE.82.066106
http://dx.doi.org/10.1103/PhysRevE.81.046110
--
Regards,
Minh Van Nguyen
http://bit.ly/mvngu
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--
Dr. Roey Angel
Max-Planck-Institute for Terrestrial Microbiology
Karl-von-Frisch-Strasse 10
D-35043 Marburg, Germany
Office: +49-(0)6421178832
Mobile: +49-(0)17661278588
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