Dear list,

in the example on Edge layers and covariates

<https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge-layers-and-covariates>,

blocks are fitted as follows:

state = gt.minimize_blockmodel_dl(g, deg_corr=False, layers=True,

state_args=dict(ec=g.ep.value,

layers=False))

I'm trying to make sure I understand the LayeredBlockState correctly. Are

the following statements correct?

1. The independent layers version is used, which means that there is one

layer for every possible number of co-appearances. *This means that

number of co-appearances is treated as a categorical, rather than an

ordinal variable. *

2. If one wanted to encourage the model to assort actors into the same

block if they have many co-appearances, the following fit would be more

appropriate:

state = gt.minimize_blockmodel_dl(g, deg_corr=False, layers=False,

state_args=dict(eweight=g.ep.value))

(If I'm right, then I find that the second model is closer to what an

applied scientist would be interested in...)

Many thanks for clearing this up,

Peter

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