Thank you Etienne, this seems to work like a charm. Also thanks to the rest
of you for your help.


Henrik

On 11 June 2010 13:51, Cuvelier Etienne <ecuscimail@gmail.com> wrote:

>
>
> 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
>>
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>>
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>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>>
>

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