Cascading failures mechanism based on betweenness-degree ratio distribution with different connecting preferences

2017 ◽  
Vol 28 (04) ◽  
pp. 1750052 ◽  
Author(s):  
Xiao Juan Wang ◽  
Shi Ze Guo ◽  
Lei Jin ◽  
Mo Chen
Keyword(s):  
Phase Transition ◽  
Failure Mechanism ◽  
Cascading Failure ◽  
Failure Model ◽  
Scale Free Network ◽  
Ratio Distribution ◽  
Vulnerable Area ◽  
Scale Free ◽  
Initial Load ◽  
Free Network

We study the structural robustness of the scale free network against the cascading failure induced by overload. In this paper, a failure mechanism based on betweenness-degree ratio distribution is proposed. In the cascading failure model we built the initial load of an edge which is proportional to the node betweenness of its ends. During the edge random deletion, we find a phase transition. Then based on the phase transition, we divide the process of the cascading failure into two parts: the robust area and the vulnerable area, and define the corresponding indicator to measure the performance of the networks in both areas. From derivation, we find that the vulnerability of the network is determined by the distribution of betweenness-degree ratio. After that we use the connection between the node ability coefficient and distribution of betweenness-degree ratio to explain the cascading failure mechanism. In simulations, we verify the correctness of our derivations. By changing connecting preferences, we find scale free networks with a slight assortativity, which performs better both in robust area and vulnerable area.

Research on cascading failure in multilayer network with different coupling preference

2017 ◽  
Vol 28 (04) ◽  
pp. 1750050 ◽  
Author(s):  
Yong Zhang ◽  
Lei Jin ◽  
Xiao Juan Wang
Keyword(s):  
Load Distribution ◽  
Random Network ◽  
Network Robustness ◽  
Cascading Failure ◽  
Failure Model ◽  
Scale Free Network ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Scale Free ◽  
Free Network

This paper is aimed at constructing robust multilayer networks against cascading failure. Considering link protection strategies in reality, we design a cascading failure model based on load distribution and extend it to multilayer. We use the cascading failure model to deduce the scale of the largest connected component after cascading failure, from which we can find that the performance of four kinds of load distribution strategies associates with the load ratio of the current edge to its adjacent edge. Coupling preference is a typical characteristic in multilayer networks which corresponds to the network robustness. The coupling preference of multilayer networks is divided into two forms: the coupling preference in layers and the coupling preference between layers. To analyze the relationship between the coupling preference and the multilayer network robustness, we design a construction algorithm to generate multilayer networks with different coupling preferences. Simulation results show that the load distribution based on the node betweenness performs the best. When the coupling coefficient in layers is zero, the scale-free network is the most robust. In the random network, the assortative coupling in layers is more robust than the disassortative coupling. For the coupling preference between layers, the assortative coupling between layers is more robust than the disassortative coupling both in the scale free network and the random network.

PHASE TRANSITION IN THE ISING MODEL ON LOCAL-WORLD EVOLVING NETWORKS

2008 ◽  
Vol 19 (11) ◽  
pp. 1717-1726
Author(s):  
WEI QIANG ◽  
GUANGDAO HU ◽  
PENGDA ZHAO
Keyword(s):  
Phase Transition ◽  
Critical Temperature ◽  
Ising Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Evolving Networks ◽  
Free Network ◽  
Effective Phase ◽  
Limiting Case ◽  
Ising Spins

We study the critical behavior of the Ising model on the local-world evolving network. Monte Carlo simulations with the standard Metropolis local update algorithms are performed extensively on the network with different parameters. Ising spins put onto network vertices exhibit an effective phase transition from ferromagnetism to paramagnetism upon heating. The critical temperature has been demonstrated to increase linearly with the average degree of the network as TC ~ 〈k〉. Simulation results on local-world evolving networks with various parameters show logarithmical relationships of the critical temperature with the size of the local world as TC ~ ln (ml), and with the size of the network as TC ~ ln (N), respectively. The latter is the generalization of the conclusion for the Ising model on the Barabási–Albert scale-free network, a limiting case of the local-world evolving network.

Phase transition and hysteresis in scale-free network traffic

2007 ◽  
Vol 75 (3) ◽  
Author(s):  
Mao-Bin Hu ◽  
Wen-Xu Wang ◽  
Rui Jiang ◽  
Qing-Song Wu ◽  
Yong-Hong Wu
Keyword(s):  
Phase Transition ◽  
Network Traffic ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

Cascading failures of interdependent networks with different k-core structures

2017 ◽  
Vol 31 (10) ◽  
pp. 1750112 ◽  
Author(s):  
Zhengcheng Dong ◽  
Yanjun Fang ◽  
Meng Tian
Keyword(s):  
Real Life ◽  
Network Evolution ◽  
Cascading Failures ◽  
Scale Free Network ◽  
Scale Free ◽  
Interdependent Networks ◽  
Mesoscale Structures ◽  
Initial Load ◽  
Free Network ◽  
Random Coupling

As one of the most common mesoscale structures in real-life networks, k-core hierarchical structure has attracted a lot of attention. Recent research about k-core always focuses on detecting influential nodes determining failure or epidemic propagation. However, few studies have attempted to understand how k-core structural properties can affect dynamic characteristics of network. In this paper, the influences of depth and coupling preferences of k-core on the cascading failures of interdependent scale-free networks are investigated. First, k-core structures of some real-life networks are analyzed, and a scale-free network evolution model with rich and successive k-core layers is proposed. Then, based on a load-based cascading model, the influence of the depth of k-core is investigated with a new evaluation index. In the end, two coupling preferences are analyzed, i.e. random coupling (RC) and assortative coupling (AC). Results show that the lower the depth is, the more robust the interdependent networks will be, and we find AC and RC perform dissimilarly when the capacity varies. Furthermore, all the effects will be affected by the initial load.

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