Spotting criticalities in Critical Infrastructure networks is a crucial task in order to implement effective protection strategies against exogenous or malicious events. Yet, most of the approaches in the literature focus on specific aspects (e.g., presence of hubs, minimum paths) and there is a need to identify tradeoffs among importance metrics that are typically clashing with each other. In this paper we propose an approach for the assessment of criticalities which combines multi-criteria decision making techniques and topological/dynamical centrality measures. In particular, we resort to the Sparse Analytic Hierarchy Process (SAHP) technique to calculate the relevance of the different metrics based on pairwise comparisons of the metrics by Subject Matter Experts (SMEs) and to merge the different metrics into a holistic indicator of node criticality/importance that takes into account all the metrics. With the aim to experimentally demonstrate the potential of the proposed approach, we consider a case study related to the Central London Tube Network. According to the experimental results, the proposed aggregated ranking exhibits negligible correlation with the single metrics being aggregated, thus suggesting that the proposed approach effectively combines the different metrics into a new perspective.

在关键基础设施网络中发现关键点是一项至关重要的任务，目的是实施有效的保护策略以应对外来或恶意事件。然而，文献中的大多数方法侧重于特定方面（例如，集线器的存在，最小路径），并且需要在通常相互冲突的重要性指标之间进行权衡。在本文中，我们提出了一种评估关键性的方法，该方法结合了多标准决策技术和拓扑/动态中心性度量。尤其是，我们采用稀疏分析层次过程（SAHP）技术，根据主题专家（SME）对指标进行成对比较，计算不同指标的相关性，并将不同指标合并为节点关键性/重要性的整体指标，考虑到所有指标。为了通过实验证明拟议方法的潜力，我们考虑与伦敦市中心地铁网络有关的案例研究。根据实验结果，所提出的汇总排名与所汇总的单个指标之间的相关性可忽略不计，因此表明所提出的方法有效地将不同指标组合到一个新的角度。为了通过实验证明拟议方法的潜力，我们考虑了与伦敦市中心地铁网络有关的案例研究。根据实验结果，所提出的汇总排名与所汇总的单个指标之间的相关性可忽略不计，从而表明所提出的方法有效地将不同指标组合到一个新的角度。为了通过实验证明拟议方法的潜力，我们考虑了与伦敦市中心地铁网络有关的案例研究。根据实验结果，所提出的汇总排名与所汇总的单个指标之间的相关性可忽略不计，从而表明所提出的方法有效地将不同指标组合到一个新的角度。