Am 15.08.21 um 21:51 schrieb dadakinda:
Hello,
Is there any way to bias the nested DC-SBM model towards assortative
partitions?
I've tested PPBlockState, but as far as I understand it distinguishes
groups only by internal vs external density via the Planted Partition
model: far more restrictive than the full SBM. I'm wondering if there is
a middle-ground where you have a SBM but the groups are biased
(required?) to be assortative.
I searched for something like this and found a couple papers: "A
Regularized Stochastic Block Model for the robust community detection in
complex networks" by Lu et al and "Assortative-Constrained Stochastic
Block Models" by Gribel et al. Unfortunately neither uses priors to
prevent overfitting, a nested formulation to address resolution limit,
etc. The latter paper restricts the intra-block connection probabilities
to be greater than inter-block connection probabilities. I'm not sure if
there is an equivalent idea for the microcanonical model. The only idea
I can think of would be to modify the likelihood in some way to make
intra-block edges more likely.
Such model variations are not implemented in the library.
Furthermore, I don't think that the microcanonical priors / integrated
likelihoods will be easy to write down in closed form for this kind of
constraint.
--
Tiago de Paula Peixoto <ti...@skewed.de>
_______________________________________________
graph-tool mailing list -- graph-tool@skewed.de
To unsubscribe send an email to graph-tool-le...@skewed.de
