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I have a large ( 36k vertices, 50k edges ) weighted bimodal bipartite graph and I would like to generate a projection that not only count the neighbors like the default weighted implementation but also sum the weights on the edges. You can think of it as a bipartite graph containing black vertices and blue vertices, where I want to conserve the original graph weights when there are only blue vertices.

enter image description here

The implementations I came across keep the orange value, I am interested on the red one (or hopefully get a bi-weighted projection).

I've looked so far in igraph, networkx and python-tool but in so far I only observed the projection counting the amount of edges.

Networkx method generic_weighted_projected_graph(B, nodes, weight_function=None) may make this viable but I can't see how (sna is new to me, although I am an so so python user).

Oeufcoque Penteano
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1 Answers1

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There is an example in the documentation you reference at https://networkx.github.io/documentation/latest/reference/generated/networkx.algorithms.bipartite.projection.generic_weighted_projected_graph.html of how to do exactly this.

It goes like this:

import networkx as nx
from networkx.algorithms import bipartite

edges = [('A1','B1',3),
         ('A1','B2',7),
         ('A2','B1',2),
         ('A2','B2',4),
         ]

B = nx.Graph()
B.add_weighted_edges_from(edges)

def my_weight(G, u, v, weight='weight'):
    w = 0
    for nbr in set(G[u]) & set(G[v]):
        w += G.edge[u][nbr].get(weight, 1) + G.edge[v][nbr].get(weight,1)
    return w

G = bipartite.generic_weighted_projected_graph(B, ['A1', 'A2'], weight_function=my_weight)


print G.edges(data=True)

output

[('A1', 'A2', {'weight': 16})]
Aric
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  • Wow >( I guess I misunderstood the code there. Thank you for pointing this out! – Oeufcoque Penteano Dec 04 '14 at 20:57
  • If anyone come across this with the same question, make sure to check that the graph is undirected (that it is trying to apply bipartite projection). While igraph ignore if it is directed and go on to the projection, this will just return a list of empty nodes. There is a method on the graph that can be call G2 = G.to_undirected() that will turn it undirected and solve the issue. – Oeufcoque Penteano Dec 05 '14 at 00:21