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 = np.arange(data.shape[1]) + data.shape[0] # nodes from columns start
> from the last A_node
>
> g = gt.Graph(directed=True) # directed or not directed... maybe not important
> at all
> g.add_vertex(len(A_nodes) + len(B_nodes)) #add all needed nodes
> partition = g.new_vertex_property('bool') # create a property indicating the
> node type
>
> for x in A_nodes:
> partition[g.vertex(x)] = 0 # set all A nodes to 0
>
> for x in B_nodes:
> partition[g.vertex(x)] = 1 # set all B to 1
>
> idx = np.nonzero(data) # take the edge values
> weights = adata.X[idx]
>
> idx = (idx[0], idx[1] + len(A_nodes)) # node number of columns need to be
> augmented by the offset
>
> g.add_edge_list(np.transpose(idx)) #add weights
>
> ew = g.new_edge_property("double")
> ew.a = weights
> g.ep['weight'] = ew
>
> Is there a more straightforward way to go?You can add the weights together with the edges in Graph.add_edge_list() via the eprops parameter, but otherwise the above is fine. Note that if your objective is to do SBM inference, it's better to make the graph undirected. Best, Tiago -- Tiago de Paula Peixoto <ti...@skewed.de>
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