networkx.algorithms.cluster.square_clustering¶
- networkx.algorithms.cluster.square_clustering(G, nodes=None)¶
Compute the squares clustering coefficient for nodes.
For each node return the fraction of possible squares that exist at the node [R104]
![C_4(v) = \frac{ \sum_{u=1}^{k_v}
\sum_{w=u+1}^{k_v} q_v(u,w) }{ \sum_{u=1}^{k_v}
\sum_{w=u+1}^{k_v} [a_v(u,w) + q_v(u,w)]}](../../_images/math/f95c51969852fa601f7a98156354ce3fd274f187.png)
where
are the number of common neighbors of 
and 
other than 
(ie squares), and
,
where 
if and are connected and 0 otherwise.Parameters : G : graph
A NetworkX graph
nodes : container of nodes, optional
Compute clustering only for specified nodes. Default is entire graph.
Returns : c4 : dictionary
A dictionary keyed by node with the square clustering coefficient value.
Notes
While
gives the probability that two neighbors of node v are
connected with each other, 
is the probability that two
neighbors of node v share a common neighbor different from v. This algorithm
can be applied to both bipartite and unipartite networks.References
[R104] (1, 2) Pedro G. Lind, Marta C. González, and Hans J. Herrmann. 2005 Cycles and clustering in bipartite networks. Physical Review E (72) 056127. Examples
>>> G=nx.complete_graph(5) >>> print(nx.square_clustering(G,0)) 1.0 >>> print(nx.square_clustering(G)) {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0}