average_clustering(G, nodes=None, weight=None, count_zeros=True)
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.
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.
Compute average clustering for nodes in this container.
The edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1.
If False include only the nodes with nonzero clustering in the average.
Average clustering
Compute the average clustering coefficient for the graph G.
>>> G = nx.complete_graph(5)See :
... print(nx.average_clustering(G)) 1.0
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.cluster.average_clustering
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them