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[igraph] partitioning a *weighted* undirected graph
From: |
Lara Michaels |
Subject: |
[igraph] partitioning a *weighted* undirected graph |
Date: |
Tue, 21 Sep 2010 08:40:08 -0700 (PDT) |
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
- [igraph] partitioning a *weighted* undirected graph,
Lara Michaels <=