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
--
Dr Peter Straka
Research Fellow (DECRA)
Dep. of Statistics | School of Mathematics & Statistics | UNSW Australia
Google Scholar
<https://scholar.google.com.au/citations?user=BV5PkWUAAAAJ&hl=en&authuser=1>
E: [email protected]
T: +61 (2) 938*5 7024 *| +1 313 757 0137
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