initial minimization of nested blockmodel

Hello everybody,
I'm new to graph-tool and nSBM, so forgive my naive question. We are still trying to understand how parameter influence our outcome. My first question is


state = gt.minimize_nested_blockmodel_dl(g)

Equal to

state = gt.minimize_nested_blockmodel_dl(g, mcmc_args=dict(niter=100))

? I’m asking as the documentation executes the two steps, but the
minimization function accepts parameters for MCMC sweep step.



No, these are not the same thing.

The function minimize_nested_blockmodel_dl() employs an aglomerative
heuristic which alternates between merging groups and moving nodes
between groups, and doing a bisection search for the optimal number of
groups. The mcmc_args argument controls only the moving of nodes between

The mcmc_sweep() function only performs moves of nodes between groups,
nothing else.