The resolution parameter in the Louvain method might make a difference. I'm pretty sure that the igraph (multilevel_community) implementation uses only a resolution parameter of 1, but this is tunable in other implementations.
Check out the examples on this page: http://mrvar.fdv.uni-lj.si/pajek/community/LouvainVOS.htm Even if igraph doesn't implement these methods, it does play very nice with pajek for import and export. The other alternative is to consider overlapping clustering routines, such as OSLOM (which at the moment is only implemented in C and is a bit tedious to work with). Take care, BERNiE Dr Bernie Hogan Research Fellow, Oxford Internet Institute University of Oxford http://www.oii.ox.ac.uk/people/hogan/ On 14 Aug 2012, at 12:05, Stijn van Dongen <[email protected]> wrote: > > Your graph seems quite dense; about (2*3M) / 7K = 857 outgoing > edges/arcs per node on average. This is *only* on average. > > If you have, in addition, a graph that has highly connected hub nodess > and/or small diameter, it could well be a very hard graph to cluster. > > I advise you to look into ways to reduce the number of edges in the > graph and make sure it does not have highly connected nodes; a simple > approach is possible if the edges have weights associated with them. (I > recommend considering (reciprocal) nearest neighbour type selection; > e.g. take the arc merge (or intersect) of all top-N sections of the arc > lists of all nodes). > > It should be possible even in the absence of weights, either with quick > and crude methods (remove nodes with many edges) or by considering the > number of triangles an edge participates in and thus establishing some > kind of weighting. > > There is another clustering algorithm that has a parameter affecting > granularity (mcl - I wrote it), but I have not yet made it available > in igraph unfortunately. Anyway, I think the considerations above are > likely more important. > > best, > Stijn > > > > On Tue, Aug 14, 2012 at 11:31:31AM -0400, Sam Steingold wrote: >> Hi, >> No matter what community detection I use (leading.eigenvector.community, >> multilevel.community &c), I get just 2 communities (I have 7k vertexes >> and ~3M edges). I want many more smaller communities (even if less >> modular), because this community detection is just one step in a long >> process. >> How do I limit the community size? >> Is there a better way to ensure that I get many communities? >> Thanks! >> -- >> Sam Steingold (http://sds.podval.org/) on Ubuntu 12.04 (precise) X >> 11.0.11103000 >> http://www.childpsy.net/ http://dhimmi.com http://mideasttruth.com >> http://americancensorship.org http://palestinefacts.org http://camera.org >> will write code that writes code that writes code for food >> >> _______________________________________________ >> igraph-help mailing list >> [email protected] >> https://lists.nongnu.org/mailman/listinfo/igraph-help > > -- > Stijn van Dongen >8< -o) O< forename pronunciation: [Stan] > EMBL-EBI /\\ Tel: +44-(0)1223-492675 > Hinxton, Cambridge, CB10 1SD, UK _\_/ http://micans.org/stijn > > _______________________________________________ > igraph-help mailing list > [email protected] > https://lists.nongnu.org/mailman/listinfo/igraph-help
_______________________________________________ igraph-help mailing list [email protected] https://lists.nongnu.org/mailman/listinfo/igraph-help
