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Re: [igraph] Community detection algorithm for (almost) full weighted gr
From: |
Tamas Nepusz |
Subject: |
Re: [igraph] Community detection algorithm for (almost) full weighted graph |
Date: |
Fri, 24 Oct 2014 19:57:04 +0200 |
User-agent: |
Mutt/1.5.23 (2014-03-12) |
> Then I want to apply a community detection algorithm which will evaluate
> graph structure mainly on edges' weights.
All the community detection algorithms in igraph are designed for sparse
networks, so if you want to use any of them, you have to convert your matrix to
a sparse one first. This can be done in multiple ways; e.g., you could drop the
connections below a certain weight, or you could keep the best (i.e. highest
weight) connections for each of your vertices. Then you can use whichever
algorithm you prefer from igraph.
If you want to keep your dense graph, you could try MCL [1] or spectral
clustering [2], but these are not implemented in igraph.
[1]: http://micans.org/mcl
[2]: http://en.wikipedia.org/wiki/Spectral_clustering
T.