scholarly journals A weighted scale-free network model with large-scale tunable clustering

2006 ◽  
Vol 55 (8) ◽  
pp. 4058
Author(s):  
Pan Zao-Feng ◽  
Wang Xiao-Fan
Keyword(s):  
Network Model ◽  
Large Scale ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

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

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 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

scholarly journals A Multilevel Simplification Algorithm for Computing the Average Shortest-Path Length of Scale-Free Complex Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Guoyong Mao ◽  
Ning Zhang
Keyword(s):  
Complex Network ◽  
Shortest Path ◽  
Path Length ◽  
Large Scale ◽  
Computation Time ◽  
Scale Free Network ◽  
Original Network ◽  
Memory Space ◽  
Scale Free ◽  
Free Network

Computing the average shortest-path length (ASPL) of a large scale-free network needs much memory space and computation time. Based on the feature of scale-free network, we present a simplification algorithm by cutting the suspension points and the connected edges; the ASPL of the original network can be computed through that of the simplified network. We also present a multilevel simplification algorithm to get ASPL of the original network directly from that of the multisimplified network. Our experiment shows that these algorithms require less memory space and time in computing the ASPL of scale-free network, which makes it possible to analyze large networks that were previously impossible due to memory limitations.

scholarly journals Analytic solution of a static scale-free network model

2005 ◽  
Vol 44 (2) ◽  
pp. 241-248 ◽  
Author(s):  
M. Catanzaro ◽  
R. Pastor-Satorras
Keyword(s):  
Network Model ◽  
Analytic Solution ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

A novel scale-free network model based on clique growth

2009 ◽  
Vol 16 (3) ◽  
pp. 474-477 ◽  
Author(s):  
Bo Wang ◽  
Xu-hua Yang ◽  
Wan-liang Wang
Keyword(s):  
Network Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Model Based ◽  
Free Network

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.

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