Compute a bipartite clustering coefficient for nodes.
The bipartie clustering coefficient is a measure of local density of connections defined as [R159]:
where \(N(N(u))\) are the second order neighbors of \(u\) in \(G\) excluding \(u\), and \(c_{uv}\) is the pairwise clustering coefficient between nodes \(u\) and \(v\).
The mode selects the function for \(c_{uv}\) which can be:
\(dot\):
\(min\):
\(max\):
Parameters: | G : graph
nodes : list or iterable (optional)
mode : string
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Returns: | clustering : dictionary
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See also
robins_alexander_clustering, square_clustering, average_clustering
References
[R159] | (1, 2) Latapy, Matthieu, Clémence Magnien, and Nathalie Del Vecchio (2008). Basic notions for the analysis of large two-mode networks. Social Networks 30(1), 31–48. |
Examples
>>> from networkx.algorithms import bipartite
>>> G = nx.path_graph(4) # path graphs are bipartite
>>> c = bipartite.clustering(G)
>>> c[0]
0.5
>>> c = bipartite.clustering(G,mode='min')
>>> c[0]
1.0