scholarly journals OPINION FORMATION ON A DETERMINISTIC PSEUDO-FRACTAL NETWORK

2004 ◽  
Vol 15 (01) ◽  
pp. 45-57 ◽  
Author(s):  
M. C. GONZÁLEZ ◽  
A. O. SOUSA ◽  
H. J. HERRMANN
Keyword(s):  
Random Network ◽  
Scale Free Network ◽  
Opinion Formation ◽  
Scale Free ◽  
Free Network ◽  
Fractal Network

The Sznajd model of socio-physics, with only a group of people sharing the same opinion can convince their neighbors, is applied to a scale-free random network modeled by a deterministic graph. We also study a model for elections based on the Sznajd model and the exponent obtained for the distribution of votes during the transient agrees with those obtained for real elections in Brazil and India. Our results are compared to those obtained using a Barabási–Albert scale-free network.

EFFECTS OF CONFIDENCE SCALE AND NETWORK STRUCTURE ON MINORITY OPINION SPREADING

2010 ◽  
Vol 21 (08) ◽  
pp. 1001-1010 ◽  
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.

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

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
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.

scholarly journals EPIDEMIC DYNAMICS ON RANDOM AND SCALE-FREE NETWORKS

2012 ◽  
Vol 54 (1-2) ◽  
pp. 3-22 ◽  
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.

scholarly journals Microscopic Numerical Simulations of Epidemic Models on Networks

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 932
Author(s):  
Yutaka Okabe ◽  
Akira Shudo
Keyword(s):  
Numerical Simulations ◽  
Random Network ◽  
Sir Model ◽  
Epidemic Models ◽  
Scale Free Network ◽  
Simple Derivation ◽  
Scale Free ◽  
Free Network ◽  
Epidemic Diseases

Mathematical models of the spread of epidemic diseases are studied, paying special attention to networks. We treat the Susceptible-Infected-Recovered (SIR) model and the Susceptible-Exposed-Infectious-Recovered (SEIR) model described by differential equations. We perform microscopic numerical simulations for corresponding epidemic models on networks. Comparing a random network and a scale-free network for the spread of the infection, we emphasize the role of hubs in a scale-free network. We also present a simple derivation of the exact solution of the SIR model.

scholarly journals Quantitative Controllability Index of Complex Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Lifu Wang ◽  
Yali Zhang ◽  
Jingxiao Han ◽  
Zhi Kong
Keyword(s):  
Complex Networks ◽  
Condition Number ◽  
Random Network ◽  
Small World ◽  
Scale Free Network ◽  
Scale Free ◽  
Network Topologies ◽  
Free Network ◽  
The Relationship ◽  
Controllability Index

In this paper, the controllability issue of complex network is discussed. A new quantitative index using knowledge of control centrality and condition number is constructed to measure the controllability of given networks. For complex networks with different controllable subspace dimensions, their controllability is mainly determined by the control centrality factor. For the complex networks that have the equal controllable subspace dimension, their different controllability is mostly determined by the condition number of subnetworks’ controllability matrix. Then the effect of this index is analyzed based on simulations on various types of network topologies, such as ER random network, WS small-world network, and BA scale-free network. The results show that the presented index could reflect the holistic controllability of complex networks. Such an endeavour could help us better understand the relationship between controllability and network topology.

ANALYSIS OF A COMPLEX NETWORK OF PHYSICS CONCEPTS

2012 ◽  
Vol 26 (28) ◽  
pp. 1250186 ◽  
Author(s):  
HYUN-JOO KIM
Keyword(s):  
Power Law ◽  
Betweenness Centrality ◽  
Random Network ◽  
The Other ◽  
Topological Properties ◽  
Scale Free Network ◽  
Scale Free ◽  
Energy Concept ◽  
Physics Concepts ◽  
Free Network

We construct the physics concepts network in which the physics concepts is considered as nodes. We find that this network is clearly not a random network but a scale-free network in which the distribution P(k) of the degree k follows a power law, P(k) ~ k-γ with exponent γ ≈ 2.41. The relevance between the physics concepts and the important concepts are studied by measuring the degree, the betweenness centrality, and the relevance strength and we find that the energy concept is most important. Also we find that the relevance strength s(k) as a function of the degree k exhibits a power-law behavior, s(k) ~ kα with the exponent α ≈ 0.15 and s(knn) as a function of the average neighbor's degree knn follows a power-law behavior, [Formula: see text] with the exponent δ ≈ 0.17 for small knn. We also measured the other quantities which describe the topological properties of the network and find that it has the hierarchical property and tree-like patterns.

Traffic dynamics on multilayer networks with two logical layers

2021 ◽  
pp. 2150109
Author(s):  
Jinlong Ma ◽  
Zhichao Sun ◽  
Yongqiang Zhang ◽  
Xiangyang Xu ◽  
Ruimei Zhao ◽  
...  
Keyword(s):  
Network Model ◽  
Random Network ◽  
Physical Layer ◽  
Random Networks ◽  
Scale Free Network ◽  
Multilayer Networks ◽  
Traffic Dynamics ◽  
Scale Free ◽  
Traffic Capacity ◽  
Free Network

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.

scholarly journals Evolution of Conformity Dynamics in Complex Social Networks

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 299 ◽  
Author(s):  
Yuhui Gong ◽  
Qian Yu
Keyword(s):  
Social Networks ◽  
Random Network ◽  
Small World ◽  
Network Size ◽  
Small World Network ◽  
Scale Free Network ◽  
Small World Networks ◽  
Dominant Group ◽  
Scale Free ◽  
Free Network

Conformity is a common phenomenon among people in social networks. In this paper, we focus on customers’ conformity behaviors in a symmetry market where customers are located in a social network. We establish a conformity model and analyze it in ring network, random network, small-world network, and scale-free network. Our simulations shown that topology structure, network size, and initial market share have significant effects on the evolution of customers’ conformity behaviors. The market will likely converge to a monopoly state in small-world networks but will form a duopoly market in scale networks. As the size of the network increases, there is a greater possibility of forming a dominant group of preferences in small-world network, and the market will converge to the monopoly of the product which has the initial selector in the market. Also, network density will become gradually significant in small-world networks.

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