Re: [igraph] partitioning a *weighted* undirected graph

[Andrew Edelman]

 Hi Lara,

4 of the 6 igraph community detection functions can handle weighted 
networks (spin glass, fast greedy, label propagation, and walktrap). Go 
to http://igraph.wikidot.com/community-detection-in-r for some example R 
scripts for community detection.
Good luck,

Andrew

Andrew Edelman, Ph.D.
NSF Postdoctoral Fellow in Bioinformatics
Zoology&  Physiology, Dept. 3166
1000 E. University Ave.
University of Wyoming
Laramie, WY 82071
Phone: (505) 238-3775
Email: address@hidden
www.unm.edu/~andrewe



On 9/21/2010 9:40 AM, Lara Michaels wrote:
> Hello fellow igraphers!
>
> I just came across igraph (the R package) and am just getting started.
>
> I have read the docs to find out how to import the data I have into igraph and 
> have chosen to put it in 'ncol' format and then just use read.graph() as 
> described here 
> [http://cneurocvs.rmki.kfki.hu/igraphbook/igraphbook-foreign.html].
>
> Now I would like to attempt some form of community detection on this dataset 
> that would take into account the fact that edges are weighted and undirected. 
> Is there a particular method in igraph that is especially well-suited for such 
> this task? I have just started reading on the topic, but it seems that most 
> algorithms were conceived to deal with unweighted graphs.
>
> Many thanks for any help!
> ~lara
>
>
>
>
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