networkx.algorithms.bipartite.cluster.clustering¶
-
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 1:
\[c_u = \frac{\sum_{v \in N(N(u))} c_{uv} }{|N(N(u))|}\]where
N(N(u))
are the second order neighbors ofu
inG
excludingu
, andc_{uv}
is the pairwise clustering coefficient between nodesu
andv
.The mode selects the function for
c_{uv}
which can be:dot
:\[c_{uv}=\frac{|N(u)\cap N(v)|}{|N(u) \cup N(v)|}\]min
:\[c_{uv}=\frac{|N(u)\cap N(v)|}{min(|N(u)|,|N(v)|)}\]max
:\[c_{uv}=\frac{|N(u)\cap N(v)|}{max(|N(u)|,|N(v)|)}\]- 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 – A dictionary keyed by node with the clustering coefficient value.
- Return type
dictionary
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
See also
robins_alexander_clustering()
,square_clustering()
,average_clustering()
References
- 1
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.