Hello Tiago and community,

I have a network that I'd like to infer a hierarchical SBM from, but it has weighted edges.

After reading the weighted SBM paper [1] 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.

1]: https://arxiv.org/pdf/1708.01432.pdf

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 `eweight`?

Thank you,

Alexander