Re: [graph-tool] distribution for highly skewed discrete edge weights

Am 25.08.20 um 00:28 schrieb sam:

wondering if Tiago or anyone else on the list can suggest any transformation-distribution combination that might help. i tried (without thinking too deeply) the transformation weight = log(weight) + 1 with real-geometric weights, but minimize_blockmodel_dl() was taking an unusually long time to fit so i escaped.

It's difficult to say much without looking at the data. But I would try to keep the nature of the covariates the same, i.e. if they are discrete before the transform, they should also be discrete afterwards. One option to reduce the variance may be to rank the values encountered, and take the rank index as the transformed covariate. YMMV. Best, Tiago -- Tiago de Paula Peixoto <tiago@skewed.de>