Universality and scaling laws in the cascading failure model with healing

Stippinger, Marcell; Kertész, János

doi:10.1103/physreve.98.042303

Cited by 9 publications

(11 citation statements)

References 14 publications

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“…In any case, analysis of real catastrophic blackouts demonstrates that failures of the control cyber-systems often plays the crucial role in their development [90,110]. Further examples of very recent studies include understanding the stability of the GC [63], finite sizes effects [102,144], and developing strategies to avoid an abrupt collapse of the system [69,126].…”

Section: Discussionmentioning

confidence: 99%

“…An interesting consequence of this model is that it exhibits multiple discontinuous transitions, but in which the number of iterations to reach the steady state diverges only in the final breakdown of the network. [125,126]. The healing process consists of creating links (red) with a probability ω between the nearest neighbors of each failed node (red).…”

Section: Strategies To Mitigate Overloadmentioning

confidence: 99%

“…Refs. [125,126] proposed a healing model to mitigate the cascade of failures in embedded spatial networks. The strategy is applied at each time step of the cascading failure.…”

Section: Mitigation By Healing Nodesmentioning

confidence: 99%

See 2 more Smart Citations

Cascading failures in complex networks

Valdez

Shekhtman

Rocca

et al. 2020

Journal of Complex Networks

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Cascading failure is a potentially devastating process that spreads on real-world complex networks and can impact the integrity of wide-ranging infrastructures, natural systems and societal cohesiveness. One of the essential features that create complex network vulnerability to failure propagation is the dependency among their components, exposing entire systems to significant risks from destabilizing hazards such as human attacks, natural disasters or internal breakdowns. Developing realistic models for cascading failures as well as strategies to halt and mitigate the failure propagation can point to new approaches to restoring and strengthening real-world networks. In this review, we summarize recent progress on models developed based on physics and complex network science to understand the mechanisms, dynamics and overall impact of cascading failures. We present models for cascading failures in single networks and interdependent networks and explain how different dynamic propagation mechanisms can lead to an abrupt collapse and a rich dynamic behaviour. Finally, we close the review with novel emerging strategies for containing cascades of failures and discuss open questions that remain to be addressed.

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Section: Discussionmentioning

confidence: 99%

Section: Strategies To Mitigate Overloadmentioning

confidence: 99%

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Cascading failures in complex networks

Valdez

Shekhtman

Rocca

et al. 2020

Journal of Complex Networks

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“…With this algorithm, large scale simulations for ER and 2-dimensional interdependent networks have been carried out, which verifies the exponents for the order parameter and its fluctuations numerically, as well as the finite avalanches [314]. Stippinger et al later generalized this algorithm to efficiently simulate interdependent networks with healing [315].…”

Section: Algorithms For Reducing Time Complexitymentioning

confidence: 83%

Percolation on complex networks: Theory and application

Li,

Liu,

Lü

et al. 2021

Preprint

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In the last two decades, network science has blossomed and influenced various fields, such as statistical physics, computer science, biology and sociology, from the perspective of the heterogeneous interaction patterns of components composing the complex systems. As a paradigm for random and semi-random connectivity, percolation model plays a key role in the development of network science and its applications. On the one hand, the concepts and analytical methods, such as the emergence of the giant cluster, the finite-size scaling, and the mean-field method, which are intimately related to the percolation theory, are employed to quantify and solve some core problems of networks. On the other hand, the insights into the percolation theory also facilitate the understanding of networked systems, such as robustness, epidemic spreading, vital node identification, and community detection. Meanwhile, network science also brings some new issues to the percolation theory itself, such as percolation of strong heterogeneous systems, topological transition of networks beyond pairwise interactions, and emergence of a giant cluster with mutual connections. So far, the percolation theory has already percolated into the researches of structure analysis and dynamic modeling in network science. Understanding the percolation theory should help the study of many fields in network science, including the still opening questions in the frontiers of networks, such as networks beyond pairwise interactions, temporal networks, and network of networks. The intention of this paper is to offer an overview of these applications, as well as the basic theory of percolation transition on network systems.

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“…In the network, cascading failure may cause catastrophic consequences [7]. Thus, the recent study on cascading failure problems in practical network has aroused wide concern [19,33,22,24,9,6].…”

mentioning

confidence: 99%

Research on cascading failure modes and attack strategies of multimodal transport network

Guo¹,

Xu²,

He³

et al. 2022

JIMO

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Cascading failure overall exists in practical network, which poses a risk of causing significant losses. Studying the effect of different cascading failure modes and attack strategies of the network is conducive to more effectively controlling the network. In the present study, the uniqueness of multimodal transport network is investigated by complying with the percolation theory, and a cascading failure model is built for the multimodal transport network by considering recovery mechanisms and dynamics. Under the three failure modes, i.e., node failure, edge failure and node-edge failure, nine attack strategies are formulated, consisting of random node attacking strategy (RNAS), highdegree attacking strategy (HDAS), high-closeness attacking strategy (HCAS), random edge attacking strategy (REAS), high-importance attacking strategy (HIAS1), high-importance attacking strategy (HIAS2), random node-edge attacking strategy (RN-EAS), high degree-importance1 attacking strategy (HD-I1AS), as well as high closeness-importance2 attacking strategy (HC-I2AS). The effect of network cascading failure is measured at the scale of the affected network that varies with the failure ratio and the network connectivity varying with the step. By conducting a simulation analysis, the results of the two indicators are compared; it is suggested that under the three failure modes, the attack strategies exhibiting high node closeness as the indicator always poses more effective damage to the network. Next, a sensitivity analysis is conducted, and it is concluded that HCAS is the most effective attack strategy. Accordingly, the subsequent study on the cascading failure of multimodal transport network should start with the nodes exhibiting high closeness to optimize the network.

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Universality and scaling laws in the cascading failure model with healing

Cited by 9 publications

References 14 publications

Cascading failures in complex networks

Cascading failures in complex networks

Percolation on complex networks: Theory and application

Research on cascading failure modes and attack strategies of multimodal transport network

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