Hello,
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"]))
Best, Arttu
Dear Arttu.
This is currently not supported, but will soon be possible with a new StackedBlockState that will appear in the next release.
Please open a feature-request issue in the website so you will be notified when this becomes available.
Best, Tiago
Am 02.02.22 um 18:46 schrieb arttu.malkamaki@helsinki.fi:
Hello,
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"]))
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Tiago de Paula Peixoto