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 _______________________________________________ graph-tool mailing list -- graph-tool@skewed.de To unsubscribe send an email to graph-tool-leave@skewed.de