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.
Returns: out – Clustering coefficient at specified nodes
Return type: 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