Am 02.04.20 um 02:07 schrieb Deklan Webster: > Saw you mentioning the latent Poisson model on Twitter. I skimmed > through what I could understand of the paper. > > Can it apply to directed graphs? I saw in the paper you were 'erasing' > the multiedges back into a simple graph. Can you erase into a simple > directed graph?
Yes. > Is it correct to say that this approach supplants degree-correction? > And, for DC vs non-DC you had a section in the docs about selecting > which one fits your network best given the entropy. Is there something > analogous here? Or, do I just try it out and see the results? It is orthogonal to degree correction; it can be applied with and without degree correction. The same model selection principles still apply. > In the paper you mentioned this can be applied to community detection. > As a user, is this as simple as instantiatingLatentMultigraphBlockState > and then everything else is pretty much the same: equilibrate with the > new multiflip, etc? Yes. There is even an example of this in the documentation. > On Twitter you mentioned the latent Poisson approach in relation to link > prediction. Over on the other thread you just recommended I use > `MeasuredBlockState.get_edge_prob`. What's the difference with > `LatentMultigraphBlockStat.get_edge_prob`? Will the latter give better > results? I see they're both subclasses of `UncertainBaseState`. MeasuredBlockState is based on a latent Poisson multigraph, but it also includes a model of the noisy measurement. LatentMultigraphBlockState assumes there is no measurement error. If you want to do link prediction, you should use the former, not the latter. Best, Tiago -- Tiago de Paula Peixoto <ti...@skewed.de>
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