Hmmm - I think I am misunderstanding fundamentally what maximal.cliques()
is doing - I assumed there should be no overlap but of course there will be
as vertices can be shared amongst cliques!  What I am trying to achieve is
effectively clustering by pulling out complete subgraphs, but vertices
should appear uniquely in the final set of clusters.  Can you suggest a way
of doing this?

 One thought I had was to recursively calculate the largest.cliques(),
delete the associated vertices,  and continue until there are no vertices
left.  However, I am still stuck with the problem that  largest.cliques()
can return more than one clique (if vector lengths are equal) and these can
contain overlapping vertices, so there still has to be a choice made of
which vertices to use...


On 18 November 2013 17:23, Gábor Csárdi <[email protected]> wrote:

> On Mon, Nov 18, 2013 at 11:57 AM, Tony Larson <[email protected]>
> wrote:
> > OK,
> > Here's a toy example (and on the latest igraph) - still not behaving as
> > expected??
>
> You need to set the random seed to make this example reproducible.
>
> >> mat <- matrix(sample(c(0,1), 100 * 100, replace = TRUE, prob = c(0.8,
> >> 0.2)), 100, 100)
> >> g <- graph.adjacency(mat, mode = "undirected", weighted = TRUE, diag =
> >> FALSE)
> >> mc <- maximal.cliques(g)
> >> any(duplicated(unlist(mc)))
>
> No, this is not good, you just create a long vector from all clique
> vertices, and there will be duplicates if there is overlap.... you
> need to check whether one is a subset of any of the others.
>
> G.
>
> [...]
>
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>



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Dr. Tony R. Larson
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