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Re: [igraph] dissimilarity-based community detection

From: Tamas Nepusz
Subject: Re: [igraph] dissimilarity-based community detection
Date: Sat, 22 Mar 2008 21:30:23 +0100


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.


On 2008.03.22., at 21:17, Kurt J wrote:

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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