Dear James, This is a very involved question, and it's a bit difficult to get to the bottom of what you want.
I'll start with the last question, which I think can be addressed more directly: Am 03.06.20 um 20:35 schrieb James Ruffle: > Alternatively, do you have any suggestions on how I would account for the > missingness in the data when constructing the model? I would suggest for you to take a look at the following paper which addresses directly the missing data problem: https://dx.doi.org/10.1103/PhysRevX.8.041011 The idea of coercing the "missingness" as edge covariates as you describe does not seem correct to me. Missing data is not data, it's lack of data. The paper above puts it like this, and of course the code is in graph-tool as well. > What is the best way to robustly compare the two SBM models and (hopefully) > illustrate the model fit is better with the conditional probability of the > event alone? You can only compare models that generate the same data. If they do, this is described in the documentation, i.e. you can compare the description length or Bayesian evidence. If the two SBMs generate different data, then the question is ill posed. It would be like comparing a discrete geometric distribution with a Gaussian; they will always be different, as they do not even have the same support. Best, Tiago -- Tiago de Paula Peixoto <ti...@skewed.de>
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