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clustering¶
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clustering
(G, nodes=None, mode='dot')¶ Compute a bipartite clustering coefficient for nodes.
The bipartie clustering coefficient is a measure of local density of connections defined as [R161]:
cu=∑v∈N(N(v))cuv|N(N(u))|where N(N(u)) are the second order neighbors of u in G excluding u, and cuv is the pairwise clustering coefficient between nodes u and v.
The mode selects the function for cuv which can be:
dot:
cuv=|N(u)∩N(v)||N(u)∪N(v)|min:
cuv=|N(u)∩N(v)|min(|N(u)|,|N(v)|)max:
cuv=|N(u)∩N(v)|max(|N(u)|,|N(v)|)Parameters: G : graph
A bipartite graph
nodes : list or iterable (optional)
Compute bipartite clustering for these nodes. The default is all nodes in G.
mode : string
The pariwise bipartite clustering method to be used in the computation. It must be “dot”, “max”, or “min”.
Returns: clustering : dictionary
A dictionary keyed by node with the clustering coefficient value.
See also
robins_alexander_clustering
,square_clustering
,average_clustering
References
[R161] (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