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 <tiago@skewed.de>