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Several forms of centrality have been proposed so far.
Parameters like average clustering coefficient, topological coefficient, average degree, power law distribution of degrees and betweenness centrality have been assessed to capture the topology of the networks.
Several concepts and measures of centrality have been proposed [27] that capture the degree of "betweenness" [37] or "closeness" [38] of a vertex within the overall network architecture.
Patient context and centrality have been identified as crucial indicators of successful integrative care[ 11].
Until now, several measures for characterizing node centrality have been proposed.
Among these techniques, different measures of node centrality have been proposed to identify functionally-critical network components.
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An example is shown in Figure 7. Closeness centrality has been used to identify the top central metabolites in genome-based large-scale metabolic networks [ 79], to compare unicellular and multicellular eukarya, to rank pathways and obtain a perspective on the evolution of metabolic organization [ 80].
Among these, the degree centrality has been one of the most widely used measures.
The degree centrality has been considered as not sufficient as a measure for the whole network and closeness centrality has not always proven as the ideal measure for measuring centrality.
The centrality has been described as highly characteristic of a hierarchical network, where patterns have been described as centralized on few individuals who attract attention from other network nodes [102].
Increasing interest in network centrality has been spurred by the growth of research on large-scale networks such as the hyperlinks of the worldwide web [61] and other online networks.
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