Research on invulnerability of the random scale-free network against cascading failure

2016 ◽  
Vol 444 ◽  
pp. 458-465 ◽  
Author(s):  
Rong-Rong Yin ◽  
Bin Liu ◽  
Hao-Ran Liu ◽  
Ya-Qian Li
Keyword(s):  
Cascading Failure ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
Random Scale

scholarly journals Characterizing the intrinsic correlations of scale-free networks

2016 ◽  
Vol 27 (03) ◽  
pp. 1650024 ◽  
Author(s):  
J. B. de Brito ◽  
C. I. N. Sampaio Filho ◽  
A. A. Moreira ◽  
J. S. Andrade
Keyword(s):  
Extreme Values ◽  
Network Models ◽  
Network Size ◽  
Scale Free Network ◽  
Scale Free ◽  
Scale Free Networks ◽  
Free Network ◽  
Degree Correlations ◽  
Multiple Edges ◽  
Random Scale

When studying topological or dynamical properties of random scale-free networks, it is tacitly assumed that degree–degree correlations are not present. However, simple constraints, such as the absence of multiple edges and self-loops, can give rise to intrinsic correlations in these structures. In the same way that Fermionic correlations in thermodynamic systems are relevant only in the limit of low temperature, the intrinsic correlations in scale-free networks are relevant only when the extreme values for the degrees grow faster than the square root of the network size. In this situation, these correlations can significantly affect the dependence of the average degree of the nearest neighbors of a given vertex on this vertices degree. Here, we introduce an analytical approach that is capable to predict the functional form of this property. Moreover, our results indicate that random scale-free network models are not self-averaging, that is, the second moment of their degree distribution may vary orders of magnitude among different realizations. Finally, we argue that the intrinsic correlations investigated here may have profound impact on the critical properties of random scale-free networks.

scholarly journals Number of cliques in random scale-free network ensembles

2006 ◽  
Vol 224 (1-2) ◽  
pp. 1-6 ◽  
Author(s):  
Ginestra Bianconi ◽  
Matteo Marsili
Keyword(s):  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
Network Ensembles ◽  
Random Scale

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.

Research on new evolution model of scale-free network

2009 ◽  
Vol 29 (5) ◽  
pp. 1230-1232
Author(s):  
Hao RAO ◽  
Chun YANG ◽  
Shao-hua TAO
Keyword(s):  
Evolution Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network
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