Hi Tiago,

I looked at the example in the document here

https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#sampling-from-the-posterior-distribution

<https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#sampling-from-the-posterior-distribution>

.

As the example shows, we can obtain vertex marginals on all hierarchical

levels, i.e. a "vertex_pie_fractions" at each level for each node. However,

I want to find the node partition at each level for each node according to

the *largest* "vertex_pie_fraction". Therefore, I use the following code

# Hierarchical node partition as a list of np.array

bs = [np.array([np.argmax(list(pv[i])[j]) for j in range(len(list(pv[i])))])

for i in range(len(pv))]

where pv is exactly the one shown in the example.

I believe the above line of code is correct since I have checked the results

in several real networks with small sizes (around 200 nodes and 500 edges at

most).

But it will take a quite a long time for a large network (30k nodes with

400k edges or more). Is there any efficient way to do the above work?

Best,

Alex