Effective measurement of network vulnerability under random and intentional attacks

Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highly interconnected dynamical units. The first approach to capture the global properties of such systems is to model them as graphs whose nodes represent the dynamical units, and whose links stand for the interactions between them. On the one hand, scientists have to cope with structural issues, such as characterizing the topology of a complex wiring architecture, revealing the unifying principles that are at the basis of real networks, and developing models to mimic the growth of a network and reproduce its structural properties. On the other hand, many relevant questions arise when studying complex networks' dynamics, such as learning how a large ensemble of dynamical systems that interact through a complex wiring topology can behave collectively. We review the major concepts and results recently achieved in the study of the structure and dynamics of complex networks, and summarize the relevant applications of these ideas in many different disciplines, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.

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“…See Refs. [33][34][35] for some recent applications of the efficiency, and Refs. [32,36] for some extensions of formula (2.6).…”

Section: Shortest Path Lengths Diameter and Betweennessmentioning

confidence: 99%

Complex networks: Structure and dynamics

et al. 2006

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“…In more recent studies, either the importance of a link is recalculated at each stage, or links and nodes are simply eliminated at random (Albert et al, 2004;Holme et al, 2002). Optimisation techniques and games theory have also been used in transport network studies to identify the worst scenarios and formulate a response (see, for example, Bell, 2000;Bell et al, 2008;Criado et al, 2004;Lou and Zhang, 2011). Alderson et al (2013) show how to evaluate the criticality of sets of components, how to assess the worst-case set of components that might be lost to a given number of simultaneous hostile attacks (or engineering failures, or losses to Mother Nature), and how to allocate limited defensive resources to minimize the maximum damage from a subsequent attack.…”

Section: Approaches Adoptedmentioning

confidence: 99%

Measuring the vulnerability of public transport networks

Rodríguez-Núñez

Palomares

2014

Journal of Transport Geography

204

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In the last years, studies on the vulnerability of public transport networks attract a growing attention because of the repercussions that incidents can have on the day-to-day functioning of a city. The aim of this paper is to develop a methodology for measuring public transport network vulnerability taking the Madrid Metro system as an example. The consequences of a disruptions of riding times or the number of missed trips are analysed for each of the network links with a full scan approach implemented in GIS (Geographic Information Systems). Using real trips distribution, each link in the network is measured for criticality, from which the vulnerability of lines and stations can be calculated. The proposed methodology also makes it possible to analyse the role of circular lines in network vulnerability and to obtain a worst-case scenario for the successive disruption of links by simulating a targeted attack on the network. Results show the presence of critical links in the southern part of the network, where line density is low and ridership high. They also highlight the importance of the circular line as an element of network robustness.

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“…Determinados estudios de la vulnerabilidad (así como de su término contrario, la robustez) de las redes de metro, consideran a la misma como un grafo con sus vértices y sus arcos (Criado, R. et al, 2004(Criado, R. et al, , 2006(Criado, R. et al, , 2008Dragu, V. et al, 2011;Han, S. et al, 2009;Kwan, K. et al, 2003;Latora, V. et al, 2002Latora, V. et al, , 2005, lo cual permitiría verificar el número de componentes que se mantienen conectados a la red o el número de trayectos disponibles que conectan cada par de vértices restantes después de una eliminación de nodos o tramos (Criado, R. et al, 2004), a través de una función de Vulnerabilidad (V), definida sólo por grafos conectados, y reconocer así sus elementos críticos para mejorar su seguridad. Este concepto está relacionado con el descenso del rendimiento de la red a causa de la eliminación de vértices y arcos o de la eficiencia de la red cuando alguno de sus elementos (nodos o arcos) se colapsa (Criado, R. et al, 2006).…”

Section: Antecedentesunclassified

Estudio De La Vulnerabilidad De Las Redes De Transporte Desde Una Perspectiva Geográfica

Núñez¹

2012

REA

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Effective measurement of network vulnerability under random and intentional attacks

Cited by 52 publications

References 13 publications

Complex networks: Structure and dynamics

Complex networks: Structure and dynamics

Measuring the vulnerability of public transport networks

Estudio De La Vulnerabilidad De Las Redes De Transporte Desde Una Perspectiva Geográfica

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