Given a situation where I'm trying to fit a LayeredBlockState with somewhat different edge weight distributions across the layers, is it possible to pass different rec_types for different layers when using LayeredBlockState? Or should I pick one that fits the aggregate distribution of my ten layers? Or is it perhaps best to convert the weights in each layer to discrete values?
This is how I'm currently doing it, but I know that "real-normal" works better for the first two layers if I fit them separately.
state = gt.minimize_blockmodel_dl(g, state = gt.LayeredBlockState, state_args = dict(deg_corr = True, overlap = True, layers = True, ec = g.ep.layer, recs = [g.ep.weight], rec_types = ["real-exponential"]))