Dear Tiago / community,
We are using the stochastic block model in a research project and trying to
formulate how we would best utilise the community structure results downstream,
and would welcome any suggestions.
We fit a nested stochastic block model with an edge weight, which gives us a
hierarchical partition. From this, we want to report not just the underlying
community structure, but also some form of corresponding weights of given
blocks, how ‘important' a given block is with respect to the edge weight, say.
Considering an example on the weighted foodweb network:
import graph_tool.all as gt; import numpy as np; import matplotlib;
g = gt.collection.ns["foodweb_baywet"]
state = gt.minimize_nested_blockmodel_dl(g, state_args=dict(recs=[g.ep.weight],
rec_types=["real-exponential"]))
This yields a hierarchical community structure, but how would you most suitably
determine what communities were ‘most’ or ‘least’
important/influential/correlated with respect to the edge weight?
I have considered whether this might be done with centrality metrics on the
blocks (or perhaps vcount and ecount data from a condensation graph on the
hierarchical blocks), but was keen to see if you had a more innovative idea...
Thank you for your advice!
James
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