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 <tiago@skewed.de>