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 Alexander T. J. Barron https://cogentmentat.github.io/academic/
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