On 26.05.2016 04:22, Rogelio Basurto wrote: > I am reading a bipartite network from a graphml file, then I do some > filtering and finally I construct the minimize_nested_blockmodel_dl as > in the example. Then I draw it, just like in the example, and it looks > great. I manage to draw it with the node names and they are fine. > > Then, I would like to check the names of the nodes in the different > blocks, by the levels they are arranged from the stochastic nested > block model. But I do not know how to do that. > > I found the function get_bstack() for the NestedBlockState object, but > the index in those vertices are from 0 to N, where N is the number of > vertices per level (of the model, not from my graph, I think), then > how do I associate my original vertex index (which has its name) to > those graphs from the different levels?
The partition of nodes in the first level is obtained via: b = state.levels[0].get_blocks() This is a vertex property map that says to which block a node of your network belongs. For example: >>> print(b[10]) 3 The above means that vertex 10 belongs to group 3. The same can be done for the higher levels of the hierarchy, i.e. >>> b = state.levels[1].get_blocks() >>> print(b[3]) 5 The group number 3 in the first level, belongs to group number 5 in the second level. An so on. Best, Tiago -- Tiago de Paula Peixoto <[email protected]>
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