[graph-tool] Effect of hubs in WSBM
Tiago de Paula Peixoto
tiago at skewed.de
Fri Jul 17 19:44:32 CEST 2020
Am 17.07.20 um 14:19 schrieb Dominik Schlechtweg:
>> is there a way to suppress the likelihood of the edge probabilities as in  where the alpha-parameter can be used to fit "only to the weight information"? (Compare to formula (4) in .)
>>  C. Aicher, A. Z. Jacobs, and A. Clauset. 2014. Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2):221–248.
> How does the graph-tools implementation relate to the alpha-parameter in formula (4)? Is it equivalent to giving equal weight to edge probabilities and weights (alpha = 0.5)?
This parameter is not implemented in graph-tool.
Note that such a parameter does not have an obvious interpretation from
a generative modelling point of view, specially in a Bayesian way. We
cannot just introduce ad-hoc parameters to cancel certain parts of the
likelihood, without paying proper attention to issues of normalization,
etc, and expect things to behave consistently.
In other words, I do not fully agree with the alpha parameter of Aicher
> Is it possible to use LatentMultigraphBlockState() with a weighted graph?
Tiago de Paula Peixoto <tiago at skewed.de>
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