问题
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.
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).
回答1:
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})]
来源:https://stackoverflow.com/questions/27265242/weighted-bimodal-bipartite-graph-projection-conserving-original-weights