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.

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 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.

An improved global dynamic routing strategy for scale-free network with tunable clustering

2016 ◽  
Vol 30 (22) ◽  
pp. 1650302 ◽  
Author(s):  
Lina Sun ◽  
Ning Huang ◽  
Yue Zhang ◽  
Yannan Bai
Keyword(s):  
Shortest Path ◽  
Path Length ◽  
Network Capacity ◽  
Network Routing ◽  
Scale Free Network ◽  
Scale Free ◽  
Global Dynamic ◽  
Routing Strategy ◽  
Traffic Awareness ◽  
Free Network

An efficient routing strategy can deliver packets quickly to improve the network capacity. Node congestion and transmission path length are inevitable real-time factors for a good routing strategy. Existing dynamic global routing strategies only consider the congestion of neighbor nodes and the shortest path, which ignores other key nodes’ congestion on the path. With the development of detection methods and techniques, global traffic information is readily available and important for the routing choice. Reasonable use of this information can effectively improve the network routing. So, an improved global dynamic routing strategy is proposed, which considers the congestion of all nodes on the shortest path and incorporates the waiting time of the most congested node into the path. We investigate the effectiveness of the proposed routing for scale-free network with different clustering coefficients. The shortest path routing strategy and the traffic awareness routing strategy only considering the waiting time of neighbor node are analyzed comparatively. Simulation results show that network capacity is greatly enhanced compared with the shortest path; congestion state increase is relatively slow compared with the traffic awareness routing strategy. Clustering coefficient increase will not only reduce the network throughput, but also result in transmission average path length increase for scale-free network with tunable clustering. The proposed routing is favorable to ease network congestion and network routing strategy design.

scholarly journals Analysis of Average Shortest-Path Length of Scale-Free Network

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Guoyong Mao ◽  
Ning Zhang
Keyword(s):  
Load Balancing ◽  
Shortest Path ◽  
Path Length ◽  
Large Scale ◽  
Programming Model ◽  
Computing Time ◽  
Coarse Grained ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

Computing the average shortest-path length of a large scale-free network needs much memory space and computation time. Hence, parallel computing must be applied. In order to solve the load-balancing problem for coarse-grained parallelization, the relationship between the computing time of a single-source shortest-path length of node and the features of node is studied. We present a dynamic programming model using the average outdegree of neighboring nodes of different levels as the variable and the minimum time difference as the target. The coefficients are determined on time measurable networks. A native array and multimap representation of network are presented to reduce the memory consumption of the network such that large networks can still be loaded into the memory of each computing core. The simplified load-balancing model is applied on a network of tens of millions of nodes. Our experiment shows that this model can solve the load-imbalance problem of large scale-free network very well. Also, the characteristic of this model can meet the requirements of networks with ever-increasing complexity and scale.

On Modeling Shortest Path Length Distribution in Scale-Free Network Topologies

2018 ◽  
Vol 12 (4) ◽  
pp. 3869-3872 ◽  
Author(s):  
Agnese V. Ventrella ◽  
Giuseppe Piro ◽  
Luigi Alfredo Grieco
Keyword(s):  
Shortest Path ◽  
Path Length ◽  
Length Distribution ◽  
Scale Free Network ◽  
Scale Free ◽  
Network Topologies ◽  
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

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
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