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Among all centrality indexes, betweenness centrality is known as one of the most common indexes [17].
All centrality measures are derived from the adjacency matrix, so they constitute different mathematical computations using the same underlying data.
Overall, all centrality measures slightly declined in their statistical relationship with urban population between 1950 and 1990, with a bigger drop between 1980 and 1990.
However, while all centrality metrics are positively correlated, there is not a strict linear relationship in any of the cases.
Although all centrality metrics were positively correlated, leverage and eigenvector both provided additional information not evident from degree or betweenness alone.
While individuals in P-networks experienced significant upward trends for all centrality metrics with respect to the R-networks, those in the B-networks were able to do so only for B- and P-centrality ranks.
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First, the fraction of units with at least one MRSA cases increased with a sigmoidal dependence of the logarithm of all the centrality measures.
The second step was to select the variables with the best explanatory ability for sentencing outcomes of all the centrality measures studied.
The results were consistent in all these centrality measures; hub genes using a combined network were more representative than hub genes using a single data source network.
2. The second step was to select the variables with the best explanatory ability for sentencing outcomes of all the centrality measures studied.
Except for the CCO, other chi-square statistic values are greater than the table value in set A, and the chi-square statistic values for all the centralities are greater than the table value in set B. It means that except for the CCO in set A, for other centralities in set A and all the centralities in set B, the fraction distributions differ from that in the whole network.
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