Robustness of network of networks under targeted attack

Dong, Gaogao; Gao, Jianxi; Du, Ruxu; Li, Tian; Stanley, H. Eugene; Havlin, Shlomo

doi:10.1103/physreve.87.052804

Cited by 202 publications

(135 citation statements)

References 43 publications

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“…In interacting networks, when a node in one network fails it usually causes dependent nodes in other networks to fail which, in turn, may cause further damage in the first network and result in a cascade of failures with catastrophic consequences. This analytical framework enables to follow the dynamic process of the cascading failures step-by-step and to derive steady state solutions [65][66][67]. This formalism provides critical new information on the resilience and vulnerabilities of real world complex systems, such as economic and financial systems.…”

Section: Discussionmentioning

confidence: 99%

Cascading Failures in Interdependent Economic Networks

Havlin

Kenett

2015

Proceedings of the International Conference on Social Modeling and Simulation, Plus Econophysics Colloquium 2014

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Throughout the past decade, there has been a significant advance in understanding the structure and function of networks, and mathematical models of networks are now widely used to describe a broad range of complex systems, such as socio-economic systems. However, the significant majority of methods have dealt almost exclusively with individual networks treated as isolated systems. In reality an individual network is often just one component in a much larger complex multi-level network (network of networks, NON). The NON framework provides critical new insights into the structure and function of real-world complex systems. One such insight is that NON system is significantly more vulnerable to shocks and damages, which has lead to the development of the theory of cascading failures in interdependent networks. Here we provide an overview of this theory, and one example of its application to economic systems. IntroductionThe growth of technology, globalization, and urbanization has caused world-wide human social and economic activities to become increasingly interdependent [1][2][3][4][5][6][7][8][9][10][11][12][13]. From the recent financial crisis it is clear that components of this complex system have become increasingly susceptible to collapse. Current models have been unable to predict instability, provide scenarios for future stability, or control or even mitigate systemic failure. Thus, there is a need of new ways of quantifying complex system vulnerabilities as well as new strategies for mitigating systemic damage and increasing system resiliency [14,15]. Achieving this would also provide new insight into such key issues as financial contagion [16,17] and systemic risk [18][19][20] and would provide a way of maintaining economic and financial stability in the future.

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Section: Discussionmentioning

confidence: 99%

Cascading Failures in Interdependent Economic Networks

Havlin

Kenett

2015

Proceedings of the International Conference on Social Modeling and Simulation, Plus Econophysics Colloquium 2014

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“…In this research area, many researchers have studied percolation processes on various types of graphs, such as random graphs, 6 networks of networks, 7 multiplex networks, 8 and interdependent networks. 9 However, the generating function approach is complex because many auxiliary variables and generating functions are necessary, differing according to the complexity of the targeted graphs.…”

Section: Related Workmentioning

confidence: 99%

Percolation analysis for constructing a robust modular topology based on a binary-dynamics model

Toyonaga

Kominami

Murata

2017

International Journal of Distributed Sensor Networks

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In the context of Internet of Things, virtualization of wireless sensor networks is a crucial technology for sharing sensors as infrastructure. In our previous work, we proposed a brain-inspired method for constructing a robust and adaptive virtual wireless sensor network topology and showed that the method of constructing links between modules has crucial effect on robustness and adaptivity of the constructed virtual wireless sensor network topology. However, the best way of constructing a robust and adaptive virtual wireless sensor network topology is still unclear. Therefore, in this article, we use an analytical approach and propose a method for clarifying robustness of a topology according to the method of constructing links between modules. We add a new tool to a binary-dynamics model which is an analytical method for investigating percolation dynamics on a modular network. Evaluation by simulation showed that graphs in which the number of nodes selected as endpoint nodes of inter-module links and the degrees of the endpoint nodes before the link addition are large have robust connectivity in terms of the point of fragmentation of the network into modules when we fix the degree of the endpoint nodes after the link addition. After the point, the internal structure of modules may matter more. We additionally investigate an applicable range of our proposed method.

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“…Based on the complex network theories and methods, most studies of robustness estimation over the last decade have focused on the analysis of the physical layer of network [5,[10][11][12][13][14][15][16]. Earlier researches regarding the robustness of network have mainly focused on network topology, such as, the node failures when being suffered from targeted attacks or random failures.…”

Section: Introductionmentioning

confidence: 99%

A Communication Network Robustness Estimation Method Based on an Improved ELM

Wen¹,

Huang²,

Wu³

2017

dtcse

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Abstract. The communication network robustness is becoming increasingly important because of its wider applications on higher reliability requirement systems, such as UAV, CBTC, etc. But communication network robustness estimation is becoming more difficult due to related various factors such as attacks, EMI, errors, mobility, fault-tolerant strategies, etc. With the purpose of estimating the robustness of communication network, most existing network robustness estimation researches mainly focus on failures in network physical layer and take complex network methods to estimate network robustness; recently, others have used Monte Carlo method to consider nodes mobility and several researches have summed up whole factors with weights to estimate the network robustness by AHP methods. Though these methods have largely been exploited to estimate the robustness of networks approximately, there is still no proper method to estimate the robustness of real communication network with consideration of nonlinear relationships among multiple factors. In this study, firstly, a double-layer network robustness model has been constructed; secondly, in order to take more robustness related factors into consideration, we have constructed a simulation platform based on NS-3; finally, wehave proposed a novel robustness estimation method based on improved ELM (extreme learning machine), which is powerful to estimate the nonlinear relationships. The novel robustness estimation method has been formed by four steps: simulation and data collection, improved EML, model training, and estimation. In the end, contrast analyzes have been made to illustrate three facts: (1) fault-tolerant strategies can improve the robustness obviously;(2) our double-layer network robustness model is better than the previous model; (3) the improved ELM has a better cross-validation accuracy which is above 95%.

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Robustness of network of networks under targeted attack

Cited by 202 publications

References 43 publications

Cascading Failures in Interdependent Economic Networks

Cascading Failures in Interdependent Economic Networks

Percolation analysis for constructing a robust modular topology based on a binary-dynamics model

A Communication Network Robustness Estimation Method Based on an Improved ELM

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