Modelling interdependent infrastructures using interacting dynamical models

Rosato, Vittorio; Issacharoff, Limor; Tiriticco, F.; Meloni, Sandro; Porcellinis, Stefano De; Setola, Roberto

doi:10.1504/ijcis.2008.016092

Cited by 453 publications

(301 citation statements)

References 12 publications

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“…produced infrastructures that are becoming more and more interdependent, and understanding how robustness is affected by these interdependences is one of the main challenges faced when designing resilient infrastructures 67,[69][70][71][72] . In recent decades, research was carried out in applied science on cataloguing, analysing and modelling the interdependences in critical infrastructure as well as modelling cascading failures in coupled critical infrastructure networks 40,[42][43][44][45][46][47][48] .…”

Section: Figure 1 | Schematic Demonstration Of First-and Second-ordermentioning

confidence: 99%

“…In interacting networks, the failure of nodes in one network generally leads to the failure of dependent nodes in other networks, which in turn may cause further damage to the first network, leading to cascading failures and catastrophic consequences. It is known, for example, that blackouts in various countries have been the result of cascading failures between interdependent systems such as communication and power grid systems 67,68 . Furthermore, different kinds of critical infrastructure are also coupled together, such as systems of water and food supply, communications, fuel, financial transactions and power generation and transmission.…”

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confidence: 99%

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Networks formed from interdependent networks

et al. 2011

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Complex networks appear in almost every aspect of science and technology. Although most results in the field have been obtained by analysing isolated networks, many real-world networks do in fact interact with and depend on other networks. The set of extensive results for the limiting case of non-interacting networks holds only to the extent that ignoring the presence of other networks can be justified. Recently, an analytical framework for studying the percolation properties of interacting networks has been developed. Here we review this framework and the results obtained so far for connectivity properties of 'networks of networks' formed by interdependent random networks.T he interdisciplinary field of network science has attracted a great deal of attention in recent years . This development is based on the enormous number of data that are now routinely being collected, modelled and analysed, concerning social 31-39 , economic 14,36,40,41 , technological 40,42-48 and biological 9,13,49,50 systems. The investigation and growing understanding of this extraordinary volume of data will enable us to make the infrastructures we use in everyday life more efficient and more robust.The original model of networks, random graph theory, was developed in the 1960s by ErdAEs and Rényi, and is based on the assumption that every pair of nodes is randomly connected with the same probability, leading to a Poisson degree distribution. In parallel, in physics, lattice networks, where each node has exactly the same number of links, have been studied to model physical systems. Although graph theory is a well-established tool in the mathematics and computer science literature, it cannot describe well modern, real-life networks. Indeed, the pioneering 1999 observation by Barabasi 2 , that many real networks do not follow the ErdAEs-Rényi model but that organizational principles naturally arise in most systems, led to an overwhelming accumulation of supporting data, new models and computational and analytical results, and to the emergence of a new science, that of complex networks.Complex networks are usually non-homogeneous structures that in many cases obey a power-law form in their degree (that is, number of links per node) distribution. These systems are called scale-free networks. Real networks that can be approximated as scale-free networks include the Internet 3 , the World Wide Web 4 , social networks 31-39 representing the relations between individuals, infrastructure networks such as those of airlines 51 , networks in biology 9,13,49,50 , in particular networks of proteinprotein interactions 10 , gene regulation and biochemical pathways, and networks in physics, such as polymer networks or the potentialenergy-landscape network. The discovery of scale-free networks led to a re-evaluation of the basic properties of networks, such as their robustness, which exhibit a drastically different character than those of ErdAEs-Rényi networks. For example, whereas homogeneous ErdAEs-Rényi networks are extremely vulnerable to random fa...

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Section: Figure 1 | Schematic Demonstration Of First-and Second-ordermentioning

confidence: 99%

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confidence: 99%

Networks formed from interdependent networks

et al. 2011

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“…For short scales (r < ζ) it behaves like an infinite dimensional system with mean-field exponents, while for longer scales (r > ζ) the two-dimensional exponents are observed. system depend on one another [18]. Therefore, it is important to understand the spatial network topology discussed above in the case of interacting networks.…”

Section: Universality Classes In a Spatial Network Modelmentioning

confidence: 99%

“…In addition, in recent years, world-wide human technological, social and economic systems have become more and more integrated and interdependent [13][14][15][16][17][18]. Therefore, there is a need of modeling these systems as interdependent for understanding their structure, function and robustness [19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Multi-Universality and Localized Attacks in Spatially Embedded Networks

Gross¹,

Vaknin²,

Danziger³

et al. 2017

Proceedings of the Asia-Pacific Econophysics Conference 2016 — Big Data Analysis and Modeling Toward Super Smart Society — (APE

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We study a realistic spatial network model constructed by randomly linking lattice sites with linklengths following an exponential distribution with a characteristic scale ζ. We find that this simple spatial network topology does not fulfill any single universality class, but exhibits a new multiuniversality with two sets of critical exponents. This bi-universality is characterized by random-like scaling laws for measurements on a scale smaller than ζ but spatial scaling for measurements on a larger scale. We further explore this topology by studying the resilience of a two-layer multiplex under localized attack. We find that for a broad range of the control parameters, our system is metastable. In this metastable region, a localized attack larger than a critical size -that does not depends on the size of the system -induces a propagating cascade of failures leading to the system collapse.

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“…Empirical works have been done to verify the effect of network coupling mostly on the level of the overall topological structure such as electrical and Internet networks in Italy (Rosato et al, 2008) or air and sea transport networks in the world . The latter work notably proposed specific measures of network coupling to test the probability for coupled nodes to share same coupled neighbors and to have comparable degree levels in each network.…”

Section: Multigraphs Multiplex Graphs and Coupled Networkmentioning

confidence: 99%