7.3.4.2. networkx.algorithms.bipartite.projection.weighted_projected_graph¶

networkx.algorithms.bipartite.projection.weighted_projected_graph(B, nodes, ratio=False)[source]¶

Returns a weighted projection of B onto one of its node sets.

The weighted projected graph is the projection of the bipartite network B onto the specified nodes with weights representing the number of shared neighbors or the ratio between actual shared neighbors and possible shared neighbors if ratio=True [R504]. The nodes retain their attributes and are connected in the resulting graph if they have an edge to a common node in the original graph.

Parameters:	B : NetworkX graph The input graph should be bipartite. nodes : list or iterable Nodes to project onto (the “bottom” nodes). ratio: Bool (default=False) If True, edge weight is the ratio between actual shared neighbors and possible shared neighbors. If False, edges weight is the number of shared neighbors.
Returns:	Graph : NetworkX graph A graph that is the projection onto the given nodes.

See also

is_bipartite, is_bipartite_node_set, sets, collaboration_weighted_projected_graph, overlap_weighted_projected_graph, generic_weighted_projected_graph, projected_graph

Notes

No attempt is made to verify that the input graph B is bipartite. The graph and node properties are (shallow) copied to the projected graph.

References

[R504]

(1, 2) Borgatti, S.P. and Halgin, D. In press. “Analyzing Affiliation Networks”. In Carrington, P. and Scott, J. (eds) The Sage Handbook of Social Network Analysis. Sage Publications.

Examples

>>> from networkx.algorithms import bipartite
>>> B = nx.path_graph(4)
>>> G = bipartite.weighted_projected_graph(B, [1,3])
>>> list(G)
[1, 3]
>>> list(G.edges(data=True))
[(1, 3, {'weight': 1})]
>>> G = bipartite.weighted_projected_graph(B, [1,3], ratio=True)
>>> list(G.edges(data=True))
[(1, 3, {'weight': 0.5})]