7.3.4.3. networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph¶

networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph(B, nodes)[source]¶

Newman’s weighted projection of B onto one of its node sets.

The collaboration weighted projection is the projection of the bipartite network B onto the specified nodes with weights assigned using Newman’s collaboration model [R502]:

\[w_{v,u} = \sum_k \frac{\delta_{v}^{w} \delta_{w}^{k}}{k_w - 1}\]

where v and u are nodes from the same bipartite node set, and w is a node of the opposite node set. The value k_w is the degree of node w in the bipartite network and delta_{v}^{w} is 1 if node v is linked to node w in the original bipartite graph or 0 otherwise.

The nodes retain their attributes and are connected in the resulting graph if have an edge to a common node in the original bipartite graph.

Parameters:	B : NetworkX graph The input graph should be bipartite. nodes : list or iterable Nodes to project onto (the “bottom” nodes).
Returns:	Graph : NetworkX graph A graph that is the projection onto the given nodes.

See also

is_bipartite, is_bipartite_node_set, sets, 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

[R502]

(1, 2) Scientific collaboration networks: II. Shortest paths, weighted networks, and centrality, M. E. J. Newman, Phys. Rev. E 64, 016132 (2001).

Examples

>>> from networkx.algorithms import bipartite
>>> B = nx.path_graph(5)
>>> B.add_edge(1,5)
>>> G = bipartite.collaboration_weighted_projected_graph(B, [0, 2, 4, 5])
>>> list(G)
[0, 2, 4, 5]
>>> for edge in G.edges(data=True): print(edge)
... 
(0, 2, {'weight': 0.5})
(0, 5, {'weight': 0.5})
(2, 4, {'weight': 1.0})
(2, 5, {'weight': 0.5})