Disrupting road networks: a case study on the vulnerabilities of the city of Messina

Robustness of complex systems, that is the capacity of a system to continue performing its functions after a major failure, is one of the most relevant topics of complex network analysis. This is studied by quantifying the impact of node and edge removal due to random and targeted attacks on network...

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Vydané v:	Applied network science Ročník 10; číslo 1; s. 51 - 23
Hlavní autori:	Ficara, Annamaria, Celesti, Antonio, Villari, Massimo, Fazio, Maria
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.12.2025 Springer Nature B.V SpringerOpen
Predmet:	Algorithms Case studies Complex network Complex systems Complexity Computer Appl. in Social and Behavioral Sciences Computer Science Computer viruses Connectivity Disruption Edge centrality Efficiency Failure Infrastructure Motor vehicles Network analysis Network disruption Random walk Road network Roads & highways Simulation and Modeling Spatial data Spatial network analysis Special Issue of the 13th International Conference on Complex Networks and their Applications Street network Topology Traffic Urban planning Spatial network analysis Road network Street network Network disruption Edge centrality Complex network
ISSN:	2364-8228, 2364-8228
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Abstract	Robustness of complex systems, that is the capacity of a system to continue performing its functions after a major failure, is one of the most relevant topics of complex network analysis. This is studied by quantifying the impact of node and edge removal due to random and targeted attacks on network connectivity. The most effective strategies for network disruption can be identified to understand which network elements to protect. In this study, we develop a graph model of the road network of Messina (Italy) based on spatial data and filtered to include only roads accessible to motor vehicles. We apply various edge centrality measures, which are commonly used in spatial network analysis, alongside novel measures based on random walks, to perform targeted attacks and compare the results accordingly to random edge removals. The topology of road networks is constrained by the physical space in which they are embedded, which limits long-range connections and shapes node degree distributions. In this paper, we evaluate and compare methods for identifying critical roads whose removal significantly disrupts network connectivity. We quantify disruption effects using several metrics, such as the size of the largest connected component and the number of connected components. Our results demonstrate that current flow edge betweenness is the most effective centrality measure for simulating connectivity disruption in the proposed road network.
AbstractList	Robustness of complex systems, that is the capacity of a system to continue performing its functions after a major failure, is one of the most relevant topics of complex network analysis. This is studied by quantifying the impact of node and edge removal due to random and targeted attacks on network connectivity. The most effective strategies for network disruption can be identified to understand which network elements to protect. In this study, we develop a graph model of the road network of Messina (Italy) based on spatial data and filtered to include only roads accessible to motor vehicles. We apply various edge centrality measures, which are commonly used in spatial network analysis, alongside novel measures based on random walks, to perform targeted attacks and compare the results accordingly to random edge removals. The topology of road networks is constrained by the physical space in which they are embedded, which limits long-range connections and shapes node degree distributions. In this paper, we evaluate and compare methods for identifying critical roads whose removal significantly disrupts network connectivity. We quantify disruption effects using several metrics, such as the size of the largest connected component and the number of connected components. Our results demonstrate that current flow edge betweenness is the most effective centrality measure for simulating connectivity disruption in the proposed road network. Abstract Robustness of complex systems, that is the capacity of a system to continue performing its functions after a major failure, is one of the most relevant topics of complex network analysis. This is studied by quantifying the impact of node and edge removal due to random and targeted attacks on network connectivity. The most effective strategies for network disruption can be identified to understand which network elements to protect. In this study, we develop a graph model of the road network of Messina (Italy) based on spatial data and filtered to include only roads accessible to motor vehicles. We apply various edge centrality measures, which are commonly used in spatial network analysis, alongside novel measures based on random walks, to perform targeted attacks and compare the results accordingly to random edge removals. The topology of road networks is constrained by the physical space in which they are embedded, which limits long-range connections and shapes node degree distributions. In this paper, we evaluate and compare methods for identifying critical roads whose removal significantly disrupts network connectivity. We quantify disruption effects using several metrics, such as the size of the largest connected component and the number of connected components. Our results demonstrate that current flow edge betweenness is the most effective centrality measure for simulating connectivity disruption in the proposed road network.
ArticleNumber	51
Author	Ficara, Annamaria Villari, Massimo Fazio, Maria Celesti, Antonio
Author_xml	– sequence: 1 givenname: Annamaria surname: Ficara fullname: Ficara, Annamaria email: aficara@unime.it organization: Department of Cognitive Sciences, Psychology, Education, and Cultural Studies, University of Messina – sequence: 2 givenname: Antonio surname: Celesti fullname: Celesti, Antonio organization: Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences, University of Messina – sequence: 3 givenname: Massimo surname: Villari fullname: Villari, Massimo organization: Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences, University of Messina – sequence: 4 givenname: Maria surname: Fazio fullname: Fazio, Maria organization: Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences, University of Messina
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Snippet	Robustness of complex systems, that is the capacity of a system to continue performing its functions after a major failure, is one of the most relevant topics... Abstract Robustness of complex systems, that is the capacity of a system to continue performing its functions after a major failure, is one of the most...
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SubjectTerms	Algorithms Case studies Complex network Complex systems Complexity Computer Appl. in Social and Behavioral Sciences Computer Science Computer viruses Connectivity Disruption Edge centrality Efficiency Failure Infrastructure Motor vehicles Network analysis Network disruption Random walk Road network Roads & highways Simulation and Modeling Spatial data Spatial network analysis Special Issue of the 13th International Conference on Complex Networks and their Applications Street network Topology Traffic Urban planning
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Title	Disrupting road networks: a case study on the vulnerabilities of the city of Messina
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Volume	10
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