Am 15.01.20 um 11:15 schrieb Davide Cittaro:

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

is

state = gt.minimize_nested_blockmodel_dl(g) state.mcmc_sweep(niter=100)

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 groups.

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