Traffic dynamics on multilayer networks with two logical layers

In order to study traffic dynamics on multilayer networks, it is of great significance to build a network model which can more exactly reflect the actual network layered structure characteristics. In this paper, a three-layer network model in which two logical layers are mapped on one physical layer is established, and the traffic capacities of three kinds of multilayer networks with different combinations of logical layers are compared. Simulation results show that when the physical layer is the same random network, the network whose logical layers are two random networks has the optimal traffic capacity, the network with one random network and one scale-free network in the logical layers has the better traffic capacity than the network whose logical layers are two scale-free networks.

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EPIDEMIC DYNAMICS ON RANDOM AND SCALE-FREE NETWORKS

The ANZIAM Journal ◽

10.1017/s1446181112000302 ◽

2012 ◽

Vol 54 (1-2) ◽

pp. 3-22 ◽

Cited By ~ 3

Author(s):

J. BARTLETT ◽

M. J. PLANK

Keyword(s):

Random Network ◽

Infectious Agent ◽

Mixed Population ◽

Random Networks ◽

Scale Free Network ◽

Final Size ◽

Scale Free ◽

Epidemic Dynamics ◽

Scale Free Networks ◽

Free Network

AbstractRandom networks were first used to model epidemic dynamics in the 1950s, but in the last decade it has been realized that scale-free networks more accurately represent the network structure of many real-world situations. Here we give an analytical and a Monte Carlo method for approximating the basic reproduction number ${R}_{0} $ of an infectious agent on a network. We investigate how final epidemic size depends on ${R}_{0} $ and on network density in random networks and in scale-free networks with a Pareto exponent of 3. Our results show that: (i) an epidemic on a random network has the same average final size as an epidemic in a well-mixed population with the same value of ${R}_{0} $; (ii) an epidemic on a scale-free network has a larger average final size than in an equivalent well-mixed population if ${R}_{0} \lt 1$, and a smaller average final size than in a well-mixed population if ${R}_{0} \gt 1$; (iii) an epidemic on a scale-free network spreads more rapidly than an epidemic on a random network or in a well-mixed population.

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EFFECTS OF CONFIDENCE SCALE AND NETWORK STRUCTURE ON MINORITY OPINION SPREADING

International Journal of Modern Physics C ◽

10.1142/s0129183110015610 ◽

2010 ◽

Vol 21 (08) ◽

pp. 1001-1010 ◽

Cited By ~ 3

Author(s):

BO SHEN ◽

YUN LIU

Keyword(s):

Simple Model ◽

Network Structure ◽

Random Network ◽

Random Networks ◽

Scale Free Network ◽

Opinion Formation ◽

Scale Free ◽

Scale Free Networks ◽

Free Network ◽

Confidence Scale

We study the dynamics of minority opinion spreading using a proposed simple model, in which the exchange of views between agents is determined by a quantity named confidence scale. To understand what will promote the success of minority, two types of networks, random network and scale-free network are considered in opinion formation. We demonstrate that the heterogeneity of networks is advantageous to the minority and exchanging views between more agents will reduce the opportunity of minority's success. Further, enlarging the degree that agents trust each other, i.e. confidence scale, can increase the probability that opinions of the minority could be accepted by the majority. We also show that the minority in scale-free networks are more sensitive to the change of confidence scale than that in random networks.

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Research on cascading failure in multilayer network with different coupling preference

International Journal of Modern Physics C ◽

10.1142/s0129183117500504 ◽

2017 ◽

Vol 28 (04) ◽

pp. 1750050 ◽

Cited By ~ 3

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.

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Network Entropy and Systemic Risk in Dynamic Banking Systems

Complexity ◽

10.1155/2017/1852897 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Liang He ◽

Shouwei Li

Keyword(s):

Systemic Risk ◽

Random Network ◽

Small World ◽

Random Networks ◽

Scale Free Network ◽

Small World Networks ◽

Scale Free ◽

Banking Systems ◽

Free Network ◽

Network Entropy

We investigate network entropy of dynamic banking systems, where interbank networks analyzed include random networks, small-world networks, and scale-free networks. We find that network entropy is positively correlated with the effect of systemic risk in the three kinds of interbank networks and that network entropy in the small-world network is the largest, followed by those in the random network and the scale-free network.

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Maximal assortative matching for real-world network graphs, random network graphs and scale-free network graphs

Vietnam Journal of Computer Science ◽

10.1007/s40595-016-0066-0 ◽

2016 ◽

Vol 3 (3) ◽

pp. 151-179 ◽

Cited By ~ 2

Author(s):

Natarajan Meghanathan

Keyword(s):

Real World ◽

Random Network ◽

Assortative Matching ◽

Scale Free Network ◽

Scale Free ◽

Free Network

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Analysis on Invulnerability of Wireless Sensor Network towards Cascading Failures Based on Coupled Map Lattice

Complexity ◽

10.1155/2018/6386324 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 4

Author(s):

Xiuwen Fu ◽

Yongsheng Yang ◽

Haiqing Yao

Keyword(s):

Random Network ◽

Small World ◽

Coupled Map Lattice ◽

Wireless Sensor ◽

Cascading Failures ◽

Scale Free Network ◽

Scale Free ◽

Network Topologies ◽

Free Network ◽

Entire Network

Previous research of wireless sensor networks (WSNs) invulnerability mainly focuses on the static topology, while ignoring the cascading process of the network caused by the dynamic changes of load. Therefore, given the realistic features of WSNs, in this paper we research the invulnerability of WSNs with respect to cascading failures based on the coupled map lattice (CML). The invulnerability and the cascading process of four types of network topologies (i.e., random network, small-world network, homogenous scale-free network, and heterogeneous scale-free network) under various attack schemes (i.e., random attack, max-degree attack, and max-status attack) are investigated, respectively. The simulation results demonstrate that the rise of interference R and coupling coefficient ε will increase the risks of cascading failures. Cascading threshold values Rc and εc exist, where cascading failures will spread to the entire network when R>Rc or ε>εc. When facing a random attack or max-status attack, the network with higher heterogeneity tends to have a stronger invulnerability towards cascading failures. Conversely, when facing a max-degree attack, the network with higher uniformity tends to have a better performance. Besides that, we have also proved that the spreading speed of cascading failures is inversely proportional to the average path length of the network and the increase of average degree k can improve the network invulnerability.

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