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Differences could be due to the use of different centrality measures, as described earlier in the text.
The examination of different centrality metrics for each node in the brain network allowed for a comparison of the similarity or dissimilarity of each method.
In [ 85] a list and definitions of 17 types of different centrality measures are presented.
Network theory also allows calculation of different centrality measures for network elements, revealing globally important network information in different contexts [ 3– 5].
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Computation of network centralities allowed a first ranking of human kinases and phosphatases according to their central role in the network (Supplementary Table S6, reporting node-by-node values of different centralities).
Different choices of α and β lead to different centrality values, resulting in different node rankings.
Table 4 p -values of Wilcoxon-Mann-Whitney test for different centrality metrics and alternative hypotheses.
Benzi et al. [27] in their paper, showed that different choices of (alpha) and (beta) lead to different centrality values.
Using the Mann Whitney U test normalized by the number of possible pairings (see Methods), we evaluated the efficacy of 16 different centrality measures to differentiate essential from non-essential genes on 18 genetic networks.
That is, instead of assigning 16 different centrality measures to every gene in a given network, the number of combined centrality measures was n!/ n-i)!*i! (n: number of individual centrality measures applied to a given network; i: number of combined centralities, i.e., i = 2, 3 or 4).
The social quality of an individual in the presented models was thus contingent on one of the three different centrality measures [12]: (1) Popularity (P; also called in-degree), (2) Closeness (C), and (3) Betweenness (B).
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