Am 19.06.2018 um 21:01 schrieb Alexandre Hannud Abdo:
> Ni! Hi Philipp,
>
> Yes, there are more straightforward paths to the same information:
>
> # get some graph and model it
> import graph_tool.all as gt
> g = gt.collection.data["celegansneural"]
> s = gt.minimize_nested_blockmodel_dl(g)
>
>
Ni! Hi Philipp,
Yes, there are more straightforward paths to the same information:
# get some graph and model it
import graph_tool.all as gt
g = gt.collection.data["celegansneural"]
s = gt.minimize_nested_blockmodel_dl(g)
# get your groups of vertices in a dictionary
l0 = s.levels[0]
(I should probably add that I am only interested in relations between the
nodes in a given block with each other, so am happy to work with vertex
filters.)
--
Sent from:
http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/
___
Hello,
I have just fitted an SBM to my graph. Having run state =
gt.minimize_nested_blockmodel_dl(g, deg_corr=True) I now would like to
investigate the results a bit more closely. More specifically I am after the
best way to access all vertices assigned to a given block.
I can use get_levels()
