Re: [igraph] dissimilarity-based community detection

[Tamas Nepusz]

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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