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 <tiago@skewed.de>