[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 [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 <tiago at skewed.de>

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