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"clustering coefficient" is a correct and commonly used term in written English
It is typically used in the context of network analysis and refers to a measure of the degree to which nodes in a network tend to cluster together. Example: "The clustering coefficient of the social media platform was high, indicating that users were highly connected and tended to form tight-knit groups within the network."
Exact(60)
% clusterCoeff = clustering coefficient for bad cells.
In panel (b) we report the clustering coefficient.
At low exponents, below 2.333, the clustering coefficient becomes high.
The following also increase: geodesic distance, clustering coefficient and local efficiency.
Clustering coefficient C (right column) and local efficiency E l (left column).
We find that the clustering coefficient is not consistently correlated with the normalized qc.
Compared with random networks, most observed networks have a higher clustering coefficient and approximately average length.
At the same time, however, coarse-grained networks tend to have a large clustering coefficient (Fig. S5(a)).
N, nodes; K, links; D, density; C, clustering coefficient; L, path length; H, heterogeneity; and M, number of connectivity modules.
(a) Number of messages sent (b) Number of logins to the platform (c) Total degree (d) Clustering coefficient.
The clustering coefficient C, measures the probability, C [0, 1], that two nodes with a common neighbor are connected together.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com