square_clustering¶

square_clustering
(G, nodes=None)[source]¶ Compute the squares clustering coefficient for nodes.
For each node return the fraction of possible squares that exist at the node [1]
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) –
 nodes (container of nodes, optional (default=all nodes in G)) – Compute clustering for nodes in this container.
Returns: c4 – A dictionary keyed by node with the square clustering coefficient value.
Return type: dictionary
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}
Notes
While (triangle clustering) 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
[1] Pedro G. Lind, Marta C. González, and Hans J. Herrmann. 2005 Cycles and clustering in bipartite networks. Physical Review E (72) 056127.