networkx.algorithms.bipartite.cluster.robins_alexander_clustering¶
-
robins_alexander_clustering
(G)[source]¶ 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 pathsL_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
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.