layers and edge covariates

graph-tool
1

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&gt;,
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

attachment.html (2.68 KB)

2

Both statements are correct.

Best,
Tiago

3

Thank you Tiago
Peter

Dear list,

in the example on 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))

Both statements are correct.

Best,
Tiago

attachment.html (3.2 KB)