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Am 23.03.20 um 15:31 schrieb Davide Cittaro:
>> Note that if your objective is to do SBM inference, it's better to make
>> the graph undirected.
>>
>
> Why so? Is this true in general?
Well, if you have a bipartite network where it is important do
Hi
> On 23 Mar 2020, at 15:24, Tiago de Paula Peixoto wrote:
>
> You can add the weights together with the edges in Graph.add_edge_list()
> via the eprops parameter, but otherwise the above is fine.
>
Ok
> Note that if your objective is to do SBM inference, it's better to make
> the graph und
Am 20.03.20 um 11:22 schrieb Davide Cittaro:
> Hello,
> I would like to test nSBM on bipartite graphs but before going on I need to
> be sure I'm able to build a bipartite graph in graph-tool starting from a
> matrix:
>
> A_nodes = np.arange(data.shape[0]) #nodes for rows start from 0
> B_nodes
Hello,
I would like to test nSBM on bipartite graphs but before going on I need to be
sure I'm able to build a bipartite graph in graph-tool starting from a matrix:
A_nodes = np.arange(data.shape[0]) #nodes for rows start from 0
B_nodes = np.arange(data.shape[1]) + data.shape[0] # nodes from col
