Previous topic


Next topic



clustering(G, nodes=None, mode='dot')[source]

Compute a bipartite clustering coefficient for nodes.

The bipartie clustering coefficient is a measure of local density of connections defined as [R122]

c_u = \frac{\sum_{v \in N(N(v))} c_{uv} }{|N(N(u))|}

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} ‘dot’:

c_{uv}=\frac{|N(u)\cap N(v)|}{|N(u) \cup N(v)|}


c_{uv}=\frac{|N(u)\cap N(v)|}{min(|N(u)|,|N(v)|)}


c_{uv}=\frac{|N(u)\cap 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.


[R122](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.


>>> from networkx.algorithms import bipartite
>>> G=nx.path_graph(4) # path is bipartite
>>> c=bipartite.clustering(G) 
>>> c[0]
>>> c=bipartite.clustering(G,mode='min') 
>>> c[0]