Universal robustness characteristic of weighted networks against cascading failure

Wang, Wen-Xu; Chen, Guanrong

doi:10.1103/physreve.77.026101

Cited by 289 publications

(131 citation statements)

References 40 publications

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“…We set the load of an edge e  in the physical layer as the sum of weights of all logical edges whose paths traverse this edge [35] . : …”

Section: The Methods Of Load Redistributionmentioning

confidence: 99%

Robustness Analysis of Layered Public Transport Networks Due to Edge Overload Breakdown

Dong¹,

Yang²,

Chen³

2014

IJITCS

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Abstract-The robustness of urban bus-transport networks has important influence on the network performance. This paper proposes the model of layered public bus-transport network which is composed of the logical layer and the physical layer and expounds the relationship between these two layers. We map the bustransport network into two spaces: space P and space L and take space P as logical layer while space L as physical layer. We define the load of edges in the physical layer according to the traffic flow in the logical layer and assume that a removed edge only leads to a redistribution of the load through it to its neighboring edges. We analysis the robustness of layered public bustransport networks in the face of cascading failure under the case of removing the edge with the highest load and redistributing of the load. Through the simulation of the public bus-transport networks of three major cities in China, we find that in the layered public bus-transport network the traffic flow in the logical layer affects the distribution of load of edges in the physical layer. The removal of the edge with the highest load may lead to the cascading failures of the physical layer, and the avalanche size decreases with the increase of the tolerance parameter.

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“…We set the load of an edge e  in the physical layer as the sum of weights of all logical edges whose paths traverse this edge [35] . : …”

Section: The Methods Of Load Redistributionmentioning

confidence: 99%

Robustness Analysis of Layered Public Transport Networks Due to Edge Overload Breakdown

Dong¹,

Yang²,

Chen³

2014

IJITCS

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“…In our model, we adopt the local weighted flow redistribution rule 30 , where we tend to allocate more loads to the higher-capacity direct neighbours of failed node to prevent more nodes from overload. Specifically, the loads of the disabled node u, represented by F u , are distributed to the nearest neighbours of node u.…”

Section: Genetic Algorithm Removal Strategy (Gars)mentioning

confidence: 99%

Cascades Tolerance of Scale-Free Networks with Attack Cost

Chen¹,

Yin²,

He³

et al. 2017

IJCIS

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Network robustness against cascades is a major topic in the fields of complex networks. In this paper, we propose an attack-cost-based cascading failure model, where the attack cost of nodes is positively related to its degree. We compare four attacking strategies: the random removal strategy (RRS), the low-degree removal strategy (LDRS), the high-degree removal strategy (HDRS) and the genetic algorithm removal strategy (GARS). It is shown that the network robustness against cascades is heavily affected by attack costs and the network exhibits the weakest robustness under GARS. We also explore the relationship between the network robustness and tolerance parameter under these attacking strategies. The simulation results indicate that the critical value of tolerance parameter under GARS is greatly larger than that of other attacking strategies. Our work can supply insight into the robustness and vulnerability of complex networks corresponding to cascading failures.

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“…Fang et al [10] investigate the cascading failures in directed complex networks and make a load redistribution rule of average allocation. Chen et al [11] propose a nearest neighbours load redistribution model, where load of broken nodes is allocated to nearest neighbours according to their degrees. Wang et al [12] propose a local load redistribution model.…”

Section: Introductionmentioning

confidence: 99%

“…And the load redistribution proportion can be seen as a function of initial loads, where = ( )/ ∑ ∈Ω ( ) and ( ) = . Actually, the load redistribution proportion [8][9][10][11][12][13][14] depends on the initial loads that reflect the load processing ability to some extent. Duan et al [17,18] explore the critical thresholds of scale-free networks against cascading failures and spatiotemporal tolerance after a fraction of nodes attacked with a tunable load redistribution model that can tune the load redistribution range and heterogeneity of the broken nodes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Load Capacity Model with a Tunable Proportion of Load Redistribution against Cascading Failures

Zhang

Song

Xia

et al. 2018

Security and Communication Networks

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Defence against cascading failures is of great theoretical and practical significance. A novel load capacity model with a tunable proportion is proposed. We take degree and clustering coefficient into account to redistribute the loads of broken nodes. The redistribution is local, where the loads of broken nodes are allocated to their nearest neighbours. Our model has been applied on artificial networks as well as two real networks. Simulation results show that networks get more vulnerable and sensitive to intentional attacks along with the decrease of average degree. In addition, the critical threshold from collapse to intact states is affected by the tunable parameter. We can adjust the tunable parameter to get the optimal critical threshold and make the systems more robust against cascading failures.

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Universal robustness characteristic of weighted networks against cascading failure

Cited by 289 publications

References 40 publications

Robustness Analysis of Layered Public Transport Networks Due to Edge Overload Breakdown

Robustness Analysis of Layered Public Transport Networks Due to Edge Overload Breakdown

Cascades Tolerance of Scale-Free Networks with Attack Cost

A Novel Load Capacity Model with a Tunable Proportion of Load Redistribution against Cascading Failures

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