Am 22.05.20 um 07:33 schrieb sam: > 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"]))
The first is the regular degree-corrected Poisson SBM. The second is a version with edge covariates, where we first sample from the Poisson SBM, and then we sample weights on edges according to a specific distribution (in your case, again a Poisson). This is not equivalent to sampling from a single Poisson, since the 'weight' Poisson covariates are sampled only on the non-zero entries of the first Poisson. So, for example, we may have a sparse graph, with most pairs of nodes not being connected, but where every edge has an integer covariate that is very high, say on the order of 100, which would not be possible to generate with model 1 without the whole graph becoming dense. Best, Tiago -- Tiago de Paula Peixoto <ti...@skewed.de>
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