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• W2996755103 abstract "Assessing critical infrastructure vulnerabilities is paramount to arrange efficient plans for their protection. Critical infrastructures are network-based systems hence, they are composed of nodes and edges. The literature shows that node criticality, which is the focus of this paper, can be addressed from different metric-based perspectives (e.g., degree, maximal flow, shortest path). However, each metric provides a specific insight while neglecting others. This paper attempts to overcome this pitfall through a methodology based on ranking aggregation. Specifically, we consider several numerical topological descriptors of the nodes’ importance (e.g., degree, betweenness, closeness, etc.) and we convert such descriptors into ratio matrices; then, we extend the Analytic Hierarchy Process problem to the case of multiple ratio matrices and we resort to a Logarithmic Least Squares formulation to identify an aggregated metric that represents a good tradeoff among the different topological descriptors. The procedure is validated considering the Central London Tube network as a case study." @default.
• W2996755103 created "2019-12-26" @default.
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• W2996755103 date "2019-12-20" @default.
• W2996755103 modified "2023-09-28" @default.
• W2996755103 title "Aggregating Centrality Rankings: A Novel Approach to Detect Critical Infrastructure Vulnerabilities" @default.
• W2996755103 cites W2020407132 @default.
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• W2996755103 doi "https://doi.org/10.1007/978-3-030-37670-3_5" @default.
• W2996755103 hasPublicationYear "2019" @default.
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