Impact of core-periphery structure on cascading failures in interdependent scale-free networks

2019 ◽  
Vol 383 (7) ◽  
pp. 607-616 ◽  
Author(s):  
Zhengcheng Dong ◽  
Meng Tian ◽  
Yuxin Lu ◽  
Jingang Lai ◽  
Ruoli Tang ◽  
...  
Keyword(s):  
Cascading Failures ◽  
Scale Free ◽  
Scale Free Networks

CASCADING FAILURES IN BARABÁSI–ALBERT SCALE-FREE NETWORKS WITH A BREAKDOWN PROBABILITY

2009 ◽  
Vol 20 (04) ◽  
pp. 585-595 ◽  
Author(s):  
JIAN-WEI WANG ◽  
LI-LI RONG
Keyword(s):  
Theoretical Analysis ◽  
Numerical Simulations ◽  
Cascading Failures ◽  
Scale Free ◽  
Scale Free Networks ◽  
Initial Load ◽  
Breakdown Probability ◽  
Proportional Relationship ◽  
Simulation Results ◽  
Failed Node

In this paper, adopting the initial load of a node j to be [Formula: see text], where kj is the degree of the node j and α is a tunable parameter that controls the strength of the initial load of a node, we propose a cascading model with a breakdown probability and explore cascading failures on a typical network, i.e., the Barabási–Albert (BA) network with scale-free property. Assume that a failed node leads only to a redistribution of the load passing through it to its neighboring nodes. According to the simulation results, we find that BA networks reach the strongest robustness level against cascading failures when α = 1 and the robustness of networks has a positive correlation with the average degree 〈k〉, not relating to the different breakdown probabilities. In addition, it is found that the robustness against cascading failures has an inversely proportional relationship with the breakdown probability of an overload node. Finally, the numerical simulations are verified by the theoretical analysis.

scholarly journals Modelling cascading failures in networks with the harmonic closeness

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0243801
Author(s):  
Yucheng Hao ◽  
Limin Jia ◽  
Yanhui Wang ◽  
Zhichao He
Keyword(s):  
Small World ◽  
Adjustable Parameter ◽  
Cascading Failures ◽  
Small World Networks ◽  
Scale Free ◽  
Low Load ◽  
Scale Free Networks ◽  
Initial Load ◽  
The Impact ◽  
Valuable Result

Many studies on cascading failures adopt the degree or the betweenness of a node to define its load. From a novel perspective, we propose an approach to obtain initial loads considering the harmonic closeness and the impact of neighboring nodes. Based on simulation results for different adjustable parameter θ, local parameter δ and proportion of attacked nodes f, it is found that in scale-free networks (SF networks), small-world networks (SW networks) and Erdos-Renyi networks (ER networks), there exists a negative correlation between optimal θ and δ. By the removal of the low load node, cascading failures are more likely to occur in some cases. In addition, we find a valuable result that our method yields better performance compared with other methods in SF networks with an arbitrary f, SW and ER networks with large f. Moreover, the method concerning the harmonic closeness makes these three model networks more robust for different average degrees. Finally, we perform the simulations on twenty real networks, whose results verify that our method is also effective to distribute the initial load in different real networks.

CASCADING FAILURES IN CONGESTED SCALE-FREE NETWORKS

2010 ◽  
Vol 21 (08) ◽  
pp. 991-999 ◽  
Author(s):  
JIAN-FENG ZHENG ◽  
LING-XIAO YANG ◽  
ZI-YOU GAO ◽  
BAI-BAI FU
Keyword(s):  
Cost Function ◽  
Shortest Paths ◽  
User Equilibrium ◽  
Cascading Failures ◽  
Traffic Flows ◽  
Scale Free ◽  
Scale Free Networks ◽  
Practical Capacity ◽  
Link Cost ◽  
Uniform Case

In this work, we study the effect of congestion on the behavior of cascading failures in scale-free networks, where a capacity is assigned on each node (controlled by a tolerance parameter α), and traffic flows are governed by user equilibrium instead of going along the shortest paths. The effect of congestion can be described by link cost function, which denotes the time needed to travel along the link. Here we focus on studying the effect of link's practical capacity, which is a parameter in link cost function. Two different kinds of link's practical capacity are investigated, i.e. uniform case and nonuniform case. In the uniform case, each link has the same value of practical capacity. While in the nonuniform case, we assume that link's practical capacity and degrees of the link's endpoints are correlated (controlled by parameter θ, which governs the heterogeneity of link's practical capacity). Simulation results show that, in the uniform case, scale-free networks are more prone to cascading failures when increasing the value of link's practical capacity. In the nonuniform case, cascading failures in scale-free networks are very sensitive to α when θ > 0; while θ < 0, scale-free networks may suffer from serious cascading failures, regardless of α.

VULNERABILITY OF EFFECTIVE ATTACK ON EDGES IN SCALE-FREE NETWORKS DUE TO CASCADING FAILURES

2009 ◽  
Vol 20 (08) ◽  
pp. 1291-1298 ◽  
Author(s):  
JIAN-WEI WANG ◽  
LI-LI RONG
Keyword(s):  
Theoretical Prediction ◽  
Numerical Simulations ◽  
Real Life ◽  
Network Robustness ◽  
Critical Threshold ◽  
Cascading Failure ◽  
Cascading Failures ◽  
Scale Free ◽  
Scale Free Networks ◽  
Initial Load

Assume the initial load of an edge ij in a network to be Lij =[(ki ∑a ∈ Γi ka)(kj ∑b ∈ Γj kb)]α with ki and kj being the degrees of the nodes connected by the edge, where α is a tunable parameter which controls the strength of the edge initial load, and Γi and Γj are the sets of neighboring nodes of i and j, respectively. We investigate the cascading phenomenon of uncorrelated scale-free networks subject to two different attacking strategies on edges, i.e. attacking on the edges with the highest loads or the lowest loads (LL). By the critical threshold of edge capacity quantifying the network robustness, we numerically discuss the effects of two attacks for the network vulnerability. Interestingly, it is found that the attack on the edge with the LL is highly effective in disrupting the structure of the attacked network when α < 0.5. In the case of α = 0.5, the effects of two attacks for the network robustness against cascading failures are almost identical. We furthermore provide the theoretical prediction support for the numerical simulations. These results may be very helpful for real-life networks to protect the key edges selected effectively to avoid cascading-failure-induced disasters.

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