I am applying community detection algorithms to a weighted network. The weight is just a number comprised between 0 and 1. What is puzzling me is that the partition returned by the multilevel.community method seems to depend on the scaling of the weights, i.e. if one multiplies the weight by 10 etc., whereas the fastgreedy.community method is not affected by this scaling issue, i.e. the partitions are always the same irrespective of the scale.
Here is the link to the data I am using https://www.dropbox.com/s/yxw7pjrsgqbfjsj/example.csv The code applied is the following: library(igraph) a <- read.csv("example.csv",header=TRUE) h <- graph.data.frame(a, directed=FALSE) h <- simplify(h, remove.multiple=TRUE, remove.loops=TRUE) memberships <- list() fg <- fastgreedy.community(h,weights=E(h)$weight) memberships$`fg` <- fg$membership fg10 <- fastgreedy.community(h,weights=E(h)$weight*10) memberships$`fg10` <- fg10$membership fg100 <- fastgreedy.community(h,weights=E(h)$weight*100) memberships$`fg100` <- fg100$membership lm <- multilevel.community(h,weights=E(h)$weight) memberships$`lm` <- lm$membership lm10 <- multilevel.community(h,weights=E(h)$weight*10) memberships$`lm10` <- lm10$membership lm100 <- multilevel.community(h,weights=E(h)$weight*100) memberships$`lm100` <- lm100$membership names=V(h)$name out <- as.data.frame(memberships) commun <- cbind(names,out) Any help would be appreciated. Thanks a lot for the wonderful package you maintain. Stefano -- Department of Management & Technology and KITes Università L. Bocconi Via Roentgen 3 20136 Milan (Italy) Grafton Building 5th floor Office B1-02 tel. +39-02-5836 3365 fax +39-02-5836 3399
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