Cascading dynamics in congested complex networks

Wang, J.; Liu, Y.-H.; Jiao, Yu; Hu, Hai‐Jie

doi:10.1140/epjb/e2009-00004-0

Cited by 22 publications

(8 citation statements)

References 22 publications

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“…The transfer of failure loads in the network is based on the connection between nodes [46], [47]. According to existing literature, propagation procedure of cascading failure is shown as the following four stages.…”

Section: B Propagation Procedures Of Cascading Failurementioning

confidence: 99%

Invulnerability Analysis of Traffic Network in Tourist Attraction Under Unexpected Emergency Events Based on Cascading Failure

Han

Wang

et al. 2019

IEEE Access

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This study aims to analyze the cascading failure process of traffic network in tourist attraction under unexpected emergency events, and discuss the measures to improve traffic network invulnerability. For that purpose, taking tourist attraction as the research object, the topology model of traffic network in tourist attraction is established based on Space L method. After different types of emergency events are simulated as different attack strategies, we discuss the spatial and temporal distribution characteristics and evolution process of tourism emergency events. The cascading failure model of traffic network based on load-capacity is constructed, then the main factors affecting the scale of dynamic cascading failures are given and their sensitivity are analyzed. Taking the Summer Palace in Beijing as an example, the invulnerability analysis of the traffic network is carried out and the node protection strategies with different network load are proposed. The study reveals that (a) the traffic network of the Summer Palace has a typical network characteristics of small world; (b) network load, node capacity, node attack strategy, adjacent nodes relationship, load distribution rule significantly affect the scale of cascading failure of the traffic network in tourist attraction, (c) when the network load coefficient δ < 0.7, the cascading failure rate (CFR) can be effectively controlled within 0.2 to avoid large-scale cascading failure; (d) the scale of cascading failure can be effectively reduced by 22.14%, 40.91%, and 63.66%, after increasing the capacity of the nodes which are the top 5%, 10%, and 20% in the ranking of CFR, respectively.

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Section: B Propagation Procedures Of Cascading Failurementioning

confidence: 99%

Invulnerability Analysis of Traffic Network in Tourist Attraction Under Unexpected Emergency Events Based on Cascading Failure

Han

Wang

et al. 2019

IEEE Access

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“…We assume therefore that worms infect vehicles through multi-hop broadcast. On the way to a worm outbreak, our work [15] refers to random attack in which worms randomly compromise nodes and intentional attack in which a selective attack targets those nodes with high degree. Different from the fixed degree distribution in wired networks, the connection relations between vehicles often change with vehicular movement, hence we infect a random vehicle and allow it to spread worms according to the LBT rule.…”

Section: Worm Propagation Modelmentioning

confidence: 99%

Modelling and simulating worm propagation in static and dynamic traffic

Wang

Liu

Deng

2014

IET Intelligent Transport Systems

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“…In network systems with objects travelling through them, such as telecommunications infrastructure, overcongestion of objects, such as data packets, can cause node failure. This can be expressed in a three stage process, from generation, to diffusion, and then dissipation [12]. Accurate and quick network control is important in networks that are working near capacity, as is expected for backbone networks of the near future [9].…”

Section: Introductionmentioning

confidence: 99%

Distributed Dynamic Measures of Criticality for Telecommunication Networks

Proselkov

Herrera

Parlikad

et al. 2021

Studies in Computational Intelligence

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Telecommunication networks are designed to route data along fixed pathways, and so have minimal reactivity to emergent loads. To service today's increased data requirements, networks management must be revolutionised so as to proactively respond to anomalies quickly and efficiently. To equip the network with resilience, a distributed design calls for node agency, so that nodes can predict the emergence of critical data loads leading to disruptions. This is to inform prognostics models and proactive maintenance planning. Proactive maintenance needs KPIs, most importantly probability and impact of failure, estimated by criticality, which is the negative impact on connectedness in a network resulting from removing some element. In this paper, we studied criticality in the sense of increased incidence of data congestion caused by a node being unable to process new data packets. We introduce three novel, distributed measures of criticality which can be used to predict the behaviour of dynamic processes occurring on a network. Their performance is compared, and tested on a simulated diffusive data transfer network. The results show potential for the distributed dynamic criticality measures to predict the accumulation of data packet loads within a communications network. These measures are predicted to be useful in proactive maintenance and routing for telecommunications, as well as informing businesses of partner criticality in supply networks.

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Cascading dynamics in congested complex networks

Cited by 22 publications

References 22 publications

Invulnerability Analysis of Traffic Network in Tourist Attraction Under Unexpected Emergency Events Based on Cascading Failure

Invulnerability Analysis of Traffic Network in Tourist Attraction Under Unexpected Emergency Events Based on Cascading Failure

Modelling and simulating worm propagation in static and dynamic traffic

Distributed Dynamic Measures of Criticality for Telecommunication Networks

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