Hi Gabor,
thanks for answering. I'm not sure i understand exactly what you mean.
I use python, but finding k-means for python would not be a problem,
the problem would be using k-means itself for graph-partitioning? what
would the feature vector be? the neighborhood?
i'm actually looking for something more suited for graphs, as you
suggest to look at community detection. By topology I in reality mean
connectivity-based graph partitioning, something that puts vertices in
cluster according to the fact they are similarly connected (in this
sense it's not SO different from the previous idea of k-means on
neighborhoods) but in a "sophisticated" way.
I'll look at the two algorithms that you suggest.
thanks!
On Mon, Dec 19, 2011 at 4:38 PM, Gábor Csárdi <
address@hidden> wrote:
Hi,
if you use R, you can use one of the built-in clustering functions,
e.g. kmeans().
But actually you can partition a graph into a given number of groups
with many of the community finding algorithms, because they return a
complete dendrogram. E.g. the fast greedy and the edge betweenness
based algorithms are like that. This might not be what you want
though, depending on what exactly you mean by 'topologically'.
Gabor
On Mon, Dec 19, 2011 at 6:16 AM, Claudio Martella
<address@hidden> wrote:
Hello,
I need to partition a weighted undirected graph topologically in a set
of K groups, something like k-means. all i can find is
community-detection without the possibility to specify K.
Can anybody give me a reference for a way of doing this in igraph or
in case to an algorithm that does so I can implement myself?
thanks!
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