> * Gábor Csárdi <[email protected]> [2012-08-15 15:23:08 -0400]: > > Maybe you don't want community detection at all, but just some kind of > clustering with a given number of clusters. If you can define some > meaningful distance measure for the vertices,
the obvious distance is the resistance one. > then you can run k-means clustering. cool! is there a function for that? (kmeans requires a numeric dataset, i.e., it works only in R^n). > Another option is to run the fast-greedy algorithm in igraph and cut > the merge dendrogram at the desired number of clusters. This does not > ensure equal cluster sizes, though. will try. thanks. (btw, why does multilevel.community return a non-hierarchical community structure?) > Btw. if you don't care much about the graph structure in your division > (= you don't care about modularity), then why don't you just create > subgraphs of equal sizes? well, I _do_ need the subgraph elements to be "similar" to each other. -- Sam Steingold (http://sds.podval.org/) on Ubuntu 12.04 (precise) X 11.0.11103000 http://www.childpsy.net/ http://camera.org http://dhimmi.com http://memri.org http://thereligionofpeace.com http://mideasttruth.com main(a){a="main(a){a=%c%s%c;printf(a,34,a,34);}";printf(a,34,a,34);} _______________________________________________ igraph-help mailing list [email protected] https://lists.nongnu.org/mailman/listinfo/igraph-help
