While BN corresponds to the number of shortest paths leading through a particular node, the latter centrality measure evaluates the changes on the average shortest path length of a graph after removal of a particular node.
However, it is a simple model to use degree centrality to evaluate the efficacy of natural products.
In this work leverage has been introduced as a new metric of network centrality to evaluate the role of individual nodes in brain networks.
Local properties are features of single vertices or edges, and, in particular, centrality coefficients are evaluated, in order to understand the importance of the nodes within the network.
We assert that system control analysis can be used as a centrality metric for evaluating the relative function of ecosystem components, and we compared the system control results to three centrality measures that are established in the literature.
In [8], the authors proposed a semi-local centrality measure which evaluated the importance of a node through a susceptible-infected-recovered (SIR) model based on the propagation velocity of a node message and the number of nodes which receive message; this effective ranking method is capable to be applied in large-scale networks.
In this sense, centrality properties were evaluated; however, 11 H-B nodes were identified in the CP-PPI network, represented only by proteins, Supplementary Table 2).
Therefore, several centrality indexes were evaluated: Betweenness, bottleneck, density of maximum neighborhood component (DMNC), node degree, edge percolation component (EPC), eccentricity, maximal clique centrality (MCC), maximum neighborhood component (MNC), radiality and stress [ 47].
Section 3.4 evaluates gene centrality based on the reachability profiles.
Therefore, we evaluated different centrality measures.1 Overall, the degree centrality displayed the strongest correlation and was therefore used as feature in our analyses.
