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# clustering¶

clustering(G, nodes=None, weight=None)[source]

Compute the clustering coefficient for nodes.

For unweighted graphs, the clustering of a node is the fraction of possible triangles through that node that exist,

where is the number of triangles through node and is the degree of .

For weighted graphs, the clustering is defined as the geometric average of the subgraph edge weights [1],

The edge weights are normalized by the maximum weight in the network .

The value of is assigned to 0 if .

Parameters: G (graph) – nodes (container of nodes, optional (default=all nodes in G)) – Compute clustering for nodes in this container. weight (string or None, optional (default=None)) – The edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1. out – Clustering coefficient at specified nodes float, or dictionary

Examples

>>> G=nx.complete_graph(5)
>>> print(nx.clustering(G,0))
1.0
>>> print(nx.clustering(G))
{0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0}


Notes

Self loops are ignored.

References

 [1] Generalizations of the clustering coefficient to weighted complex networks by J. Saramäki, M. Kivelä, J.-P. Onnela, K. Kaski, and J. Kertész, Physical Review E, 75 027105 (2007). http://jponnela.com/web_documents/a9.pdf