Re: [igraph] dissimilarity-based community detection

[Kurt J]

Thanks Tamas for your quick response.  Two things, however...  

1 - I am using python and just easy_installed igraph-0.4.5.  igraph.Graph.community_walktrap() does not seem to be present - do i need the development version??

2 - I think I mis-represented my intent by poorly framing my question in the previous email.  I already have a "dissimilarity" matrix - which is based on audio data (independent of network structure) rather than a random walk or brownian particle as in Zhou '03.  I am really only interested in the "back-half" of the algorithm, using the dissimilarity matrix and a threshold value to detect communities...

I suppose the best option is to implement this using numpy and igraph.Clustering() object?

Cheers,
Kurt J

On Sat, Mar 22, 2008 at 8:30 PM, Tamas Nepusz <address@hidden> wrote:

Hi,

Although I don't know the algorithm you mentioned (I just downloaded
the paper from arXiv), my first impression is that the method of
Latapy & Pons is similar to that in the sense that it is also based on
random walks and it can also take edge weights and directionality into
account. The difference is that the method of Latapy & Pons starts
from isolated vertices and joins them one by one based on a similar
dissimilarity measure and optimizes the modularity of the partition.
For their paper, see the following reference:

Pascal Pons, Matthieu Latapy: Computing communities in large networks
using random walks, http://arxiv.org/abs/physics/0512106

This algorithm is implemented in igraph. If you intend to use igraph
from C, the corresponding function is igraph_community_walktrap(). The
R interface refers to it as walktrap.community, the Python interface
calls it Graph.community_walktrap(). In Ruby, it is
graph.community_walktrap.

Regards,
--
Tamas

On 2008.03.22., at 21:17, Kurt J wrote:
> Hi,
>
> I'm working with audio data and a network of musicians.  I have a n
> x n dissimilarity matrix derived from audio analysis and I want to
> use it to detect community in the musician friendship network.  The
> algorithm described by H. Zhou 2003 (http://prola.aps.org/abstract/PRE/v67/i6/e061901
> ) seems a good choice.  Is something like this implemented in igraph?
>
>
> -Kurt J

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> igraph-help mailing list
> address@hidden
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