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The metrics selected are giant composition, the shortest path to the most central node, degree centrality, eigenvector centrality, page rank centrality, betweenness centrality, closeness centrality, clustering coefficient, structural equivalence to the most central node and community average centrality [10, 15, 17, 18, 24, 25].
How the centrality of the most central node exceeds the centrality of all other nodes.
This metric is based on (1) how the centrality of the most central node exceeds the centrality of all other nodes and (2) the comparison to a star network.
To justify this choice, Table 1 shows the difference in the Degree and Eigenvector centralities between the most central node and the other top ten most important nodes in the network.
Network density is given by D = 1 N ∑ i N d i. Degree centralization DC measures how central its most central node (highest degree) is in relation to how central all the other nodes are.
The most central node is the root.
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Table 6 gives the in- and outdegree centrality of the most central nodes for each community.
Betweenness centrality is widely used to identify the most central nodes in a network, which are associated to those nodes that act as bridges between the other nodes.
Betweenness centrality is widely used to identify the most central nodes in a network, which are associated with those nodes that act as bridges between the other nodes.
Moreover it is also proven that Katz centrality is computationally efficient for filtering out the most central nodes, especially in the case of large directed networks [18].
By computing centrality measures in the RCT networks we can identify the most central nodes and analyze their characteristics.
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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