bipartite graphs

graph-tool
1

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?

d

2

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

3

Hi

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 undirected.

Why so? Is this true in general?

d

4

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