average_clustering#
- average_clustering(G, nodes=None, weight=None, count_zeros=True)[source]#
Compute the average clustering coefficient for the graph G.
The clustering coefficient for the graph is the average,
\[C = \frac{1}{n}\sum_{v \in G} c_v,\]where \(n\) is the number of nodes in
G.- Parameters:
- Ggraph
- nodescontainer of nodes, optional (default=all nodes in G)
Compute average clustering for nodes in this container.
- weightstring 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.
- count_zerosbool
If False include only the nodes with nonzero clustering in the average.
- Returns:
- avgfloat
Average clustering
Notes
This is a space saving routine; it might be faster to use the clustering function to get a list and then take the average.
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
[2]Marcus Kaiser, Mean clustering coefficients: the role of isolated nodes and leafs on clustering measures for small-world networks. https://arxiv.org/abs/0802.2512
Examples
>>> G = nx.complete_graph(5) >>> print(nx.average_clustering(G)) 1.0 ----