Dear Agus,
currently the following community detection algorithms are implemented
in igraph:
fastgreedy.community
walktrap.community
spinglass.community
edge.betweenness.community
leading.eigenvector.community
Please see their manual pages for references.
spinglass.community and leading.eigenvector.community returns the
communities themselves, the other three methods return dendrograms,
you need to cut these to get the actual communities. A fairly standard
way is to cut at the highest modularity value, the modularity values
are returned by the fastgreedy and the walktrap methods (if you give
the modularity=TRUE argument). For the edge betweenness based method
you have calculate them by calling 'modularity'. See the example at
http://igraph.sf.net -> Screenshots -> Creating animations.
These methods use different heuristics to maximize modularity, if you
get consistent results with the different methods that is a good sign.
Btw. these questions are more suited to the igraph-help mailing list,
see http://igraph.sf.net -> Community -> igraph-help
Best Regards,
Gabor
On Mon, Nov 3, 2008 at 2:44 PM, Agustin Lobo <address@hidden> wrote:
Dear Gabor Csardi,
I've been trying your R package igraph, but feel a bit
lost in the methods, so I'm taking the liberty of
seeking your advice.
I have a matrix of transportation (nb of trips, T) among cities. I've
defined
t(i,j) = T(i,j)/sum(t(i,.)
Then I've created a graph with:
g3 <- graph.adjacency(fluxmetr.contir, weighted=TRUE,mode="directed")
My goal is to find communities defined by those cities that most interact.
I've tried
g3w <- walktrap.community(g3)
community.to.membership(g3, g3w$merges, steps=9)
but I'm not really understanding what I'm doing. Could you point me
to some references and/or advice on which method would be best suited for my
goal?
Thanks!
Agus
--
Dr. Agustin Lobo
Institut de Ciencies de la Terra "Jaume Almera" (CSIC)
LLuis Sole Sabaris s/n
08028 Barcelona
Spain
Tel. 34 934095410
Fax. 34 934110012
email: address@hidden
http://www.ija.csic.es/gt/obster