I've got a question about the following example from cookbook
I work on my own network in exact same way, trying to perform sampling to
some metrics. But the results are in some way replicates the behaviour from
the cookbook example:
For both cases (simple and nested SBM) the marginal distributions for
vertices most of the times has too many non-zero values for different
clusters, hence the colouring is so fine granular. Only few (1-2) clusters
obey some explicit dominant group membership. But the rest of clusters
exhibit very distributed marginals.
Do you have any explanation for this?
In case of my network I also have only 1-3 groups of nodes with some
explicit dominant group membership. And the rest of vertices has too many
non-zero, almost uniformly distributed marginals. I was thinking that for
the simple cookbook example it's not natural that some vertices has more
than 10 non-zero marginal values.
May be it's just the result of independent launches of mcmc algorithm and
random nature of groups labelling? Or there is some intuition behind this
high marginal variance in group membership?
I launched several times the optimisation, and drew the results.
Topologically the outputs were very close to each other, although colouring
was always different except a few kind of "stable" vertices. Hence, I
guess, the resulted marginals for them have the same properties. But labels
are not informative it selves. May be there is some trick how to force
some deterministic labelling policies to stabilise it ?
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