Am 17.07.20 um 14:19 schrieb Dominik Schlechtweg: >> is there a way to suppress the likelihood of the edge probabilities as in >> [2] where the alpha-parameter can be used to fit "only to the weight >> information"? (Compare to formula (4) in [2].) >> [...] >> [2] 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 et al. > Is it possible to use LatentMultigraphBlockState() with a weighted graph? Not yet. Best, Tiago -- Tiago de Paula Peixoto <ti...@skewed.de>
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