Hello Tiago and community,
I have a network that I'd like to infer a hierarchical SBM from, but it has
After reading the weighted SBM paper  and looking at the graph-tool
docs, I think I might be able to use `*minimize_nested_blockmodel_dl*` and
pass the `*BlockState*` `*eweight*` argument via `*state_args*`. The
weights are discrete and non-negative, so maybe I also need to specify "
discrete-geometric" via `*rec_types`*. The edge weights could also be seen
as multiple edges in a multigraph.
After searching through old mailing list posts, here is my current attempt
for the weighted case:
g = Graph() # Add vertices here # Add edges here edge_weights = g.new_edge_property('int') # Specify edge weights here state = minimize_nested_blockmodel_dl(g, state_args=dict(eweight=edge_weights, rec_types=['discrete-geometric']) )
Am I on the right track? And would it be better to specify the edge
weights via the `*BlockState*` `*recs*` parameter instead of using `
Alexander T. J. Barron
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