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Note

This documents the development version of NetworkX. Documentation for the current release can be found here.

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networkx.algorithms.bipartite.cluster.robins_alexander_clustering

robins_alexander_clustering(G)

Compute the bipartite clustering of G.

Robins and Alexander 1 defined bipartite clustering coefficient as four times the number of four cycles C_4 divided by the number of three paths L_3 in a bipartite graph:

\[CC_4 = \frac{4 * C_4}{L_3}\]

Parameters

G (graph) – a bipartite graph

Returns

clustering – The Robins and Alexander bipartite clustering for the input graph.

Return type

float

Examples

>>> from networkx.algorithms import bipartite
>>> G = nx.davis_southern_women_graph()
>>> print(round(bipartite.robins_alexander_clustering(G), 3))
0.468

See also

latapy_clustering(), square_clustering()

References

1

Robins, G. and M. Alexander (2004). Small worlds among interlocking directors: Network structure and distance in bipartite graphs. Computational & Mathematical Organization Theory 10(1), 69–94.