Am 17.07.20 um 19:44 schrieb Tiago de Paula Peixoto: > 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.
Thanks for clarifying this. Last question: Does your doubt also concern the special case where alpha = 0, i.e., ignoring edge probabilities completely? (This is the actually interesting case for us. We are not interested in tuning this parameter in any way.) > >> Is it possible to use LatentMultigraphBlockState() with a weighted graph? > > Not yet. We will open a feature request then, in case there is none yet. > > Best, > Tiago > > > _______________________________________________ > graph-tool mailing list > graph-tool@skewed.de > https://lists.skewed.de/mailman/listinfo/graph-tool >
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