scholarly journals Onset of power-law scaling behavior in idiotypic random and scale-free networks

2012 ◽  
Vol 376 (45) ◽  
pp. 3158-3163
Author(s):  
Elder S. Claudino ◽  
M.L. Lyra ◽  
Iram Gleria ◽  
Paulo R.A. Campos ◽  
Delvis Bertrand
Keyword(s):  
Power Law ◽  
Scaling Behavior ◽  
Scale Free ◽  
Scale Free Networks

scholarly journals Scale-free networks with the power-law exponent between 1 and 3

2007 ◽  
Vol 56 (10) ◽  
pp. 5635
Author(s):  
Guo Jin-Li ◽  
Wang Li-Na
Keyword(s):  
Power Law ◽  
Scale Free ◽  
Power Law Exponent ◽  
Scale Free Networks

scholarly journals Modeling and Analysis of New Products Diffusion on Heterogeneous Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Shuping Li ◽  
Zhen Jin
Keyword(s):  
Decision Making ◽  
Numerical Analysis ◽  
Power Law ◽  
Heterogeneous Networks ◽  
New Products ◽  
Positive Equilibrium ◽  
Modeling And Analysis ◽  
Scale Free ◽  
Scale Free Networks ◽  
Law Degree

We present a heterogeneous networks model with the awareness stage and the decision-making stage to explain the process of new products diffusion. If mass media is neglected in the decision-making stage, there is a threshold whether the innovation diffusion is successful or not, or else it is proved that the network model has at least one positive equilibrium. For networks with the power-law degree distribution, numerical simulations confirm analytical results, and also at the same time, by numerical analysis of the influence of the network structure and persuasive advertisements on the density of adopters, we give two different products propagation strategies for two classes of nodes in scale-free networks.

Effect of connection on transport between scale free networks

2017 ◽  
Vol 28 (05) ◽  
pp. 1750064 ◽  
Author(s):  
A. Ould Baba ◽  
O. Bamaarouf ◽  
A. Rachadi ◽  
H. Ez-Zahraouy
Keyword(s):  
Numerical Simulations ◽  
Power Law ◽  
Preferential Attachment ◽  
Electrical Conductance ◽  
Global Network ◽  
Scale Free ◽  
Scale Free Networks ◽  
Conductance Distribution

Using numerical simulations, we investigate the effects of the connectivity and topologies of network on the quality of transport between connected scale free networks. Hence, the flow as the electrical conductance between connected networks is calculated. It is found that the conductance distribution between networks follow a power law [Formula: see text] where [Formula: see text] is the exponent of the global Network of network, we show that the transport in the symmetric growing preferential attachment connection is more efficient than the symmetric static preferential attachment connection. Furthermore, the differences of transport and networks communications properties in the different cases are discussed.

Scale-Free Networks Based on the Value of Interest

2013 ◽  
Vol 753-755 ◽  
pp. 2959-2962
Author(s):  
Jun Tao Yang ◽  
Hui Wen Deng
Keyword(s):  
Network Model ◽  
Power Law ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Scale Free ◽  
Distribution Property ◽  
Scale Free Networks ◽  
Free Model ◽  
Free Network ◽  
Interest Model

Assigning the value of interest to each node in the network, we give a scale-free network model. The value of interest is related to the fitness and the degree of the node. Experimental results show that the interest model not only has the characteristics of the BA scale-free model but also has the characteristics of fitness model, and the network has a power-law distribution property.

scholarly journals The use of the power law for designing wireless networks

2018 ◽  
Vol 21 ◽  
pp. 00012
Author(s):  
Andrzej Paszkiewicz
Keyword(s):  
Wireless Networks ◽  
Power Law ◽  
Degree Distribution ◽  
Limited Resources ◽  
Random Networks ◽  
New Approach ◽  
Available Bandwidth ◽  
Scale Free ◽  
Scale Free Networks ◽  
Node Removal

The paper concerns the use of the scale-free networks theory and the power law in designing wireless networks. An approach based on generating random networks as well as on the classic Barabási-Albert algorithm were presented. The paper presents a new approach taking the limited resources for wireless networks into account, such as available bandwidth. In addition, thanks to the introduction of opportunities for dynamic node removal it was possible to realign processes occurring in wireless networks. After introduction of these modifications, the obtained results were analyzed in terms of a power law and the degree distribution of each node.

GROWING HIERARCHICAL SCALE-FREE NETWORKS BY MEANS OF NONHIERARCHICAL PROCESSES

2007 ◽  
Vol 17 (07) ◽  
pp. 2447-2452 ◽  
Author(s):  
S. BOCCALETTI ◽  
D.-U. HWANG ◽  
V. LATORA
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Rate Equations ◽  
Scale Free ◽  
Scale Free Networks ◽  
Degree Correlations ◽  
Law Degree ◽  
Hierarchical Scale

We introduce a fully nonhierarchical network growing mechanism, that furthermore does not impose explicit preferential attachment rules. The growing procedure produces a graph featuring power-law degree and clustering distributions, and manifesting slightly disassortative degree-degree correlations. The rigorous rate equations for the evolution of the degree distribution and for the conditional degree-degree probability are derived.

LOAD DISTRIBUTION IN CONGESTED SCALE-FREE NETWORKS

2009 ◽  
Vol 20 (02) ◽  
pp. 197-207 ◽  
Author(s):  
JIAN-FENG ZHENG ◽  
ZI-YOU GAO ◽  
BAI-BAI FU
Keyword(s):  
Power Law ◽  
Load Distribution ◽  
Cost Functions ◽  
Scale Free ◽  
Load Distributions ◽  
Scale Free Networks ◽  
Practical Capacity ◽  
Congestion Effect ◽  
Link Cost ◽  
Uniform Case

In this work, we study the effects of scale-free topology and congestion on load distribution. Congestion effect can be described by link cost functions, which map link flows into travel times. Two different kinds of link's practical capacity (it is similar to link's capacity for transport) which is a parameter in link cost functions, i.e., uniform case and nonuniform case, are investigated. After introducing the effect of congestion, load distribution is typically discussed in Barábasi–Albert and Goh scale-free networks. In the uniform case, for Barábasi–Albert scale-free networks, we recover a power-law behavior for load distribution with a larger exponent, as compared with the distribution of betweenness centrality; for Goh scale-free networks, we also recover a power-law behavior and its exponent approaches to the exponent of degree distribution. While in the nonuniform case, the power-law behavior for load distribution may not always be conserved in both Barábasi–Albert and Goh scale-free networks. That is to say, different kinds of load distributions are obtained under different conditions. It may shed some light to study traffic dynamics on scale-free networks.

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