Thanks. Could you clarify the difference between (1) loading a graph with parallel edges g and running minimize_blockmodel_dl(g) on it (2) loading a graph with simple edges but an edge property equal to the number of parallel edges and running: state_args=dict(recs=[g.ep.weight], rec_types=["discrete-poisson"])) Sorry if this question is obvious; I've read your "Nonparametric weighted stochastic block models", "Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models", and "Nonparametric Bayesian inference of the microcanonical stochastic block model" papers. I am just not 100% clear on the mapping from the papers to what the function does, in this case. I am getting some quite different results depending on whether I use method (1) or (2), but that may be due to randomness. -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/