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

From: Andrew Edelman
Subject: Re: [igraph] partitioning a *weighted* undirected graph
Date: Tue, 21 Sep 2010 10:33:11 -0600
User-agent: Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US; rv: Gecko/20100915 Lightning/1.0b2 Thunderbird/3.1.4

 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 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

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 

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!

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