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This research is focused on the optimization problem, namely, to identify the most critical nodes.
The three nonpolynomial-time algorithms identify the most critical nodes (global optimum).
minimize disruptions in sensor or logistics networks by providing backup capacity to the most critical nodes.
The (mathrm{NEGD}) in Algorithm A may be replaced with (mathrm{NEMS}) or (mathrm{NLCO}) to identify the most critical nodes.
It is necessary to apply limited resources to the most critical nodes to maximize the effect of either protecting a system or destroying a criminal or terrorist organization.
Three nonpolynomial algorithms (Sect. "Nonpolynomial-time CNI algorithms") use the three metrics to identify the most critical nodes (i.e., global optimum).
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Results from network metrics and simulated disruptive scenarios reveal Asnaes power plant as the most critical node in the system.
begin{aligned}&mathrm{Bet}( {v_k })=mathop sum limits _{i=1}^n mathop sum limits _{j=i+1}^n b_{ij} ( {v_k })end{aligned} (4) begin{aligned}&b_{ij} ( {v_k })=frac{1}{g_{ij} }times g_{ij} ( {v_k }) end{aligned} (5)The random node removal randomly selects a node as the most critical node and removes it from the system.
The measurement of the betweenness centrality and the analysis of the vulnerability allowed us to identify the most critical anatomical nodes in the brain, revealing quantitative information about the global damage that could be caused by a hypothetical failure of these nodes.
Especially the most critical ones.
Unsurprisingly, Fig. 5 shows that influential nodes are most critical.
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