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.

THE RELEVANCE-STRENGTH IN A SCALE-FREE NETWORK

2008 ◽  
Vol 22 (31) ◽  
pp. 3053-3059 ◽  
Author(s):  
HYUN-JOO KIM
Keyword(s):  
Network Model ◽  
Shortest Path ◽  
Power Law ◽  
Path Length ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
The Mean

We introduce a new quantity, relevance-strength which describes the relevance of a node to the others in a scale-free network. We define a weight between two nodes i and j based on the shortest path length between them and the relevance-strength of a node is defined as the sum of the weights between it and others. For the Barabási and Albert model which is a well-known scale-free network model, we measure the relevance-strength of each node and study the correlations with other quantities, such as the degree, the mean degree of neighbors of a node, and the mean relevance-strength of neighbors. We find that the relevance-strength shows power law behaviors and the crossover behaviors for the degree and the mean relevance-strength of neighbors. Also, we study the scaling behaviors of the relevance-strength for various average relevance-strength for all nodes.

scholarly journals COMPLEX NETWORK SIMULATION OF FOREST NETWORK SPATIAL PATTERN IN PEARL RIVER DELTA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1445-1449
Author(s):  
Y. Zeng
Keyword(s):  
Power Law ◽  
Pearl River Delta ◽  
Pearl River ◽  
Network Node ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Network Nodes ◽  
Scale Free ◽  
Node Distribution ◽  
Free Network

Forest network-construction uses for the method and model with the scale-free features of complex network theory based on random graph theory and dynamic network nodes which show a power-law distribution phenomenon. The model is suitable for ecological disturbance by larger ecological landscape Pearl River Delta consistent recovery. Remote sensing and GIS spatial data are available through the latest forest patches. A standard scale-free network node distribution model calculates the area of forest network’s power-law distribution parameter value size; The recent existing forest polygons which are defined as nodes can compute the network nodes decaying index value of the network’s degree distribution. The parameters of forest network are picked up then make a spatial transition to GIS real world models. Hence the connection is automatically generated by minimizing the ecological corridor by the least cost rule between the near nodes. Based on scale-free network node distribution requirements, select the number compared with less, a huge point of aggregation as a future forest planning network’s main node, and put them with the existing node sequence comparison. By this theory, the forest ecological projects in the past avoid being fragmented, scattered disorderly phenomena. The previous regular forest networks can be reduced the required forest planting costs by this method. For ecological restoration of tropical and subtropical in south China areas, it will provide an effective method for the forest entering city project guidance and demonstration with other ecological networks (water, climate network, etc.) for networking a standard and base datum.

scholarly journals Evolution of Cooperation through Power Law Distributed Conflicts

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Pilwon Kim
Keyword(s):  
Power Law ◽  
Evolution Of Cooperation ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Homogeneous Population ◽  
Interpersonal Conflicts ◽  
Personal Resource ◽  
Scale Free ◽  
Individual Level ◽  
Free Network

At an individual level, cooperation can be seen as a behaviour that uses personal resource to support others or the groups which one belongs to. In a conflict between two individuals, a selfish person gains an advantage over a cooperative opponent, while in a group-group conflict the group with more cooperators wins. In this work, we develop a population model with continual conflicts at various scales and show cooperation can be sustained even when interpersonal conflicts dominate, as long as the conflict size follows a power law. The power law assumption has been met in several observations from real-world conflicts. Specifically if the population is structured on a scale-free network, both the power law distribution of conflicts and the survival of cooperation can be naturally induced without assuming a homogeneous population or frequent relocation of members. On the scale-free network, even when most people become selfish from continual person-person conflicts, people on the hubs tend to remain unselfish and play a role as “repositories” of cooperation.

The Barabási and Albert scale-free network model

2018 ◽  
Vol 35 (1) ◽  
pp. 123-132 ◽  
Author(s):  
Lei Zhu ◽  
Lei Wang ◽  
Xiang Zheng ◽  
Yuzhang Xu
Keyword(s):  
Network Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

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 OPTIMIZATION OF ROBUSTNESS OF SCALE-FREE NETWORK TO RANDOM AND TARGETED ATTACKS

2005 ◽  
Vol 19 (16) ◽  
pp. 785-792 ◽  
Author(s):  
JIAN-GUO LIU ◽  
ZHONG-TUO WANG ◽  
YAN-ZHONG DANG
Keyword(s):  
Network Design ◽  
Scale Free Network ◽  
Design Guideline ◽  
Scale Free ◽  
Targeted Attacks ◽  
Maximum Value ◽  
Scale Free Networks ◽  
Free Network ◽  
Random Failures ◽  
Connectivity Distribution

Scale-free networks, having connectivity distribution P(k)~k-α (where k is the site connectivity), are very resilient to random failures but are fragile to intentional attacks. The purpose of this paper is to find the network design guideline which can make the robustness of the network to both random failures and intentional attacks maximum while keeping the average connectivity <k> per node constant. We find that when <k> = 3 the robustness of the scale-free networks reach its maximum value if the minimal connectivity m = 1, but when <k> is larger than four, the networks will become more robust to random failures and targeted attacks as the minimal connectivity m gets larger.

scholarly journals Geography in a scale-free network model

2002 ◽  
Vol 66 (5) ◽  
Author(s):  
C. P. Warren ◽  
L. M. Sander ◽  
I. M. Sokolov
Keyword(s):  
Network Model ◽  
Scale Free Network ◽  
Scale Free ◽  
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.

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