Abnormal cascading failure spreading on complex networks

Wang, Jianwei; Sun, Enhui; Xu, Biao; Li, Peng; Ni, Chengzhang

doi:10.1016/j.chaos.2016.08.007

Cited by 22 publications

(3 citation statements)

References 61 publications

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“…, the strength of the heterogeneity of the distribution of the load makes the edges with the higher load more important. These are consistent with previous studies that attacking the components with the highest load can bring the most serious damage to the network [3], [4], [8], [17], which is a special case of our model. In addition, as the value  increases, another interesting observation is that the HL curves and the LL curves have the trend of getting closer in Fig.…”

Section: Analysis and Simulation Resultssupporting

confidence: 93%

Robustness of Random Scale-Free Networks against Cascading Failure under Edge Attacks

Dong¹,

Tan²

2016

JCM

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The effect of two different edge attacks on the robustness of random scale-free networks against cascading failure is investigated by establishing a cascading failure model for random scale-free networks. In this model, the initial load of an edge is defined as a nonlinear function of the product of the betweenness of its end nodes with an adjustable parameter, and the local preferential redistribution rule is applied to assign the broken edge's load. An interesting conclusion is reached through theoretical analyses and numerical simulations: there is a threshold of the load parameter. When the value of the load parameter is larger than this threshold, attacking the edges with the higher load can result in larger cascading failures; while for the case of the parameter value smaller than the threshold, attacking the edges with the lower load will be more likely to lead to global collapse. Furthermore, the threshold value has a close relation with the degree exponent of the network. This work will be not only helpful to protect the key edges selected effectively to resist the cascading failure, but also useful in the design of high-robustness networks in order to stand against all kinds of attacks. 

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Section: Analysis and Simulation Resultssupporting

confidence: 93%

Robustness of Random Scale-Free Networks against Cascading Failure under Edge Attacks

Dong¹,

Tan²

2016

JCM

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“…In practice, when a logistics node fails, in order to limit the impact on the neighbouring nodes and to enhance the robustness of the logistics system, the packages handled will be allocated to those linked nodes according to the strength in business relations. Then the business-oriented load redistribution strategy can be noted as follows, which is different from previous works that allocate based on equal load-share policy (Scala and Lucentini, 2016) or by the preferential probability of the degree of the node (Wang et al, 2016). (4) where, represents the total load intensity of the node with its neighbouring nodes, is the additional load of node reallocated from node .…”

Section: Load Redistribution Mechanism For Cascading Failurementioning

confidence: 99%

The resilience of logistics network against node failures

Chen

Sun

Yin

et al. 2022

International Journal of Production Economics

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“…In particular, the problem of cascades with load redistribution on networked systems has aroused widespread concern 16,17,18,19 . Some important aspects of cascades have been extensively researched, including the models for describing the cascading failures 20,21,22 , the defense and control strategies for cascades 23,24,25,26 , the cascading models in real networks 27,28,29 .…”

Section: Introductionmentioning

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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Abnormal cascading failure spreading on complex networks

Cited by 22 publications

References 61 publications

Robustness of Random Scale-Free Networks against Cascading Failure under Edge Attacks

Robustness of Random Scale-Free Networks against Cascading Failure under Edge Attacks

The resilience of logistics network against node failures

Cascades Tolerance of Scale-Free Networks with Attack Cost

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