[graph-tool] Problem on bipartite-networks with label
I am using [SBMtopicmodeling](https://github.com/martingerlach/hSBM_Topicmodel)
to inference partition on bipartite networks
After a recent graph-tool update I noticed there is a problem with
**state_args** in minimize_nested_blockmodel_dl
step to reproduce:
given a trivial bi-partite network, with VertexProperty **kind** that imposes
the bi-partition
clabel = g.vp['kind']
state_args = {}
state = gt.minimize_nested_blockmodel_dl(g, state_args=state_args)
finds partitions
but when I impose bi-partition
clabel = model.g.vp['kind']
state_args = {'clabel': clabel, 'pclabel': clabel}
state = gt.minimize_nested_blockmodel_dl(g, state_args=state_args)
it fails to find any partition.
What could be the problem?
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[graph-tool] Question about entropy
Hello, I am running minimize_nested_blockmodel_dl on a certain network and obtaining some partition, good. Then I add some nodes to the network and I do another run of minimize_nested_blockmodel_dl and obtain another partition. The question I want to answer is: does adding those nodes help me finding a better partition? I am going to compare NestedBlockState.entropy() of the two run, but I am not sure this is correct. How should I take into account the fact that the networks are slightly different? Thank you in advance for any help Best Regards, Filippo Valle -- Indirizzo istituzionale di posta elettronica degli studenti e dei laureati dell'Università degli Studi di TorinoOfficial University of Turin email address for students and graduates ___ graph-tool mailing list graph-tool@skewed.de https://lists.skewed.de/mailman/listinfo/graph-tool
