Dear Graph-Tool Community,
I am interested in analysing the hierarchical partitions generated by the nested blockmodel. Specifically, after I have generated a nested SBM; I would then like to post-process this and calculate measures such as eigenvector centrality for a given hierarchical node; save this as a property, and then in visualisation apply either a size or colormap constraint to said node weighted by its centrality.
Using the collection data;
g = gt.collection.data["celegansneural”]
state = gt.minimize_nested_blockmodel_dl(g)
I can then ascertain what my levels are with;
l1state = state.levels.g
l2state = state.levels.g
I can then calculate eigenvector centrality of a given hierarchical partition as follows;
ee1, x1 = gt.eigenvector(l1state)
ee2, x2 = gt.eigenvector(l2state)
1) This presumably then needs to be saved as a hvprops(?!). But, I am unclear how to do this, not least in a way that I know for sure that the correct hierarchical vertices within l1state and l2state are aligning to the generated centrality measures of x1 and x2, respectively.
2) Furthermore, if/when that is achieved, how can I call upon this in drawing, for example to size the level 1 hierarchical vertices according to centrality, or level 2 vertices by another measure, etc.?
Hugely grateful for any solutions!