Le 11/06/2010 12:45, Henrik Aldberg a écrit :
I have a directed graph which is represented as a matrix on the form
0 4 0 1
6 0 0 0
0 1 0 5
0 0 4 0
Each row correspond to an author (A, B, C, D) and the values says how many
times this author have cited the other authors. Hence the first row says
that author A have cited author B four times and author D one time. Thus the
matrix represents two groups of authors: (A,B) and (C,D) who cites each
other. But there is also a weak link between the groups. In reality this
matrix is much bigger and very sparce but it still consists of distinct
groups of authors.
My problem is that when I cluster the matrix using pam, clara or agnes the
algorithms does not find the obvious clusters. I have tried to turn it into
a dissimilarity matrix before clustering but that did not help either.
The layout of the clustering is not that important to me, my primary
interest is the to get the right nodes into the right clusters.
Hello Henrik,
You can use a graph clustering using the igraph package.
Example:
library(igraph)
simM<-NULL
simM<-rbind(simM,c(0, 4, 0, 1))
simM<-rbind(simM,c(6, 0, 0, 0))
simM<-rbind(simM,c(0, 1, 0, 5))
simM<-rbind(simM,c(0, 0, 4, 0))
G <- graph.adjacency( simM,weighted=TRUE,mode="directed")
plot(G,layout=layout.kamada.kawai)
### walktrap.community
wt <- walktrap.community(G, modularity=TRUE)
wmemb <- community.to.membership(G, wt$merges,
steps=which.max(wt$modularity)-1)
V(G)$color <- rainbow(3)[wmemb$membership+1]
plot(G)
I hope it helps
Etienne
Sincerely
Henrik
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
