The impact of a network split on cascading failure processes

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Cascading failure models are typically used to capture the phenomenon where failures possibly trigger further failures in succession, causing knock-on effects. In many networks, this ultimately leads to a disintegrated network where the failure propagation continues independently across the various components. In order to gain insight into the impact of network splitting on cascading failure processes, we extend a well-established cascading failure model for which the number of failures obeys a power-law distribution. We assume that a single line failure immediately splits the network in two components and examine its effect on the power-law exponent. The results provide valuable qualitative insights that are crucial first steps toward understanding more complex network splitting scenarios.
Originele taal-2Engels
Pagina's (van-tot)392-416
Aantal pagina's25
TijdschriftStochastic Systems
Volume9
Nummer van het tijdschrift4
DOI's
StatusGepubliceerd - 2019

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Complex networks

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title = "The impact of a network split on cascading failure processes",
abstract = "Cascading failure models are typically used to capture the phenomenon where failures possibly trigger further failures in succession, causing knock-on effects. In many networks, this ultimately leads to a disintegrated network where the failure propagation continues independently across the various components. In order to gain insight into the impact of network splitting on cascading failure processes, we extend a well-established cascading failure model for which the number of failures obeys a power-law distribution. We assume that a single line failure immediately splits the network in two components and examine its effect on the power-law exponent. The results provide valuable qualitative insights that are crucial first steps toward understanding more complex network splitting scenarios.",
author = "Fiona Sloothaak and Zwart, {A.P. (Bert)} and Borst, {Sem C.}",
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The impact of a network split on cascading failure processes. / Sloothaak, Fiona; Zwart, A.P. (Bert); Borst, Sem C.

In: Stochastic Systems, Vol. 9, Nr. 4, 2019, blz. 392-416.

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

TY - JOUR

T1 - The impact of a network split on cascading failure processes

AU - Sloothaak, Fiona

AU - Zwart, A.P. (Bert)

AU - Borst, Sem C.

PY - 2019

Y1 - 2019

N2 - Cascading failure models are typically used to capture the phenomenon where failures possibly trigger further failures in succession, causing knock-on effects. In many networks, this ultimately leads to a disintegrated network where the failure propagation continues independently across the various components. In order to gain insight into the impact of network splitting on cascading failure processes, we extend a well-established cascading failure model for which the number of failures obeys a power-law distribution. We assume that a single line failure immediately splits the network in two components and examine its effect on the power-law exponent. The results provide valuable qualitative insights that are crucial first steps toward understanding more complex network splitting scenarios.

AB - Cascading failure models are typically used to capture the phenomenon where failures possibly trigger further failures in succession, causing knock-on effects. In many networks, this ultimately leads to a disintegrated network where the failure propagation continues independently across the various components. In order to gain insight into the impact of network splitting on cascading failure processes, we extend a well-established cascading failure model for which the number of failures obeys a power-law distribution. We assume that a single line failure immediately splits the network in two components and examine its effect on the power-law exponent. The results provide valuable qualitative insights that are crucial first steps toward understanding more complex network splitting scenarios.

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DO - 10.1287/stsy.2019.0035

M3 - Article

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SP - 392

EP - 416

JO - Stochastic Systems

JF - Stochastic Systems

SN - 1946-5238

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