scholarly journals OPTIMIZATION OF SCALE-FREE NETWORK FOR RANDOM FAILURES

2006 ◽  
Vol 20 (14) ◽  
pp. 815-820 ◽  
Author(s):  
JIAN-GUO LIU ◽  
ZHONG-TUO WANG ◽  
YAN-ZHONG DANG
Keyword(s):  
Network Design ◽  
Degree Distribution ◽  
Design Guidelines ◽  
Network Robustness ◽  
Optimization Strategy ◽  
Scale Free Network ◽  
Scale Free ◽  
Optimal Value ◽  
Free Network ◽  
Random Failures

It has been found that the networks with scale-free degree distribution are very resilient for random failures. The purpose of this work is to determine the network design guidelines which maximize the network robustness for random failures when the average number of links per node of the network is constant. The optimal value of the degree distribution exponent and the minimum connectivity to different network sizes are given in this paper. Finally, the optimization strategy on how to improve the evolving network robustness is given.

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.

URBAN TRANSIT SYSTEM AS A SCALE-FREE NETWORK

2004 ◽  
Vol 18 (19n20) ◽  
pp. 1043-1049 ◽  
Author(s):  
JIANJUN WU ◽  
ZIYOU GAO ◽  
HUIJUN SUN ◽  
HAIJUN HUANG
Keyword(s):  
Traffic Congestion ◽  
Traffic Accidents ◽  
Scale Free Network ◽  
Urban Transit ◽  
Transit System ◽  
Transit Network ◽  
Scale Free ◽  
Free Network ◽  
System Data ◽  
Random Failures

Many systems can be represented by networks as a set of nodes joined together by links indicating interaction. Recently studies have suggested that a lot of real networks are scale-free, such as the WWW, social networks, etc. In this paper, discoveries of scale-free characteristics are reported on the network constructed from the real urban transit system data in Beijing. It is shown that the connectivity distribution of the transit network decays as a power-law, and the exponent λ is about equal to 2.24 from the simulation graph. Based on the scale-free network topology structure of the transit network, if only transit "hub nodes" are controlled well, the transit network can resist random failures (such as traffic congestion, traffic accidents, etc.) successfully.

Optimization Model Research on WSNs Intrusion Tolerance with Scale-Free Topology

2014 ◽  
Vol 1049-1050 ◽  
pp. 2059-2062
Author(s):  
Xun Li Fan ◽  
Fei Fei Du ◽  
Jun Guo ◽  
Jie Zhang
Keyword(s):  
Scale Free Network ◽  
Intrusion Tolerance ◽  
Scale Free ◽  
And Topology ◽  
Model Research ◽  
Optimal Network ◽  
Free Network ◽  
Improved Model ◽  
Power Law Index ◽  
Random Failures

This paper proposes a new scale-free network BA Improved Model (BAIM) for the scale-free topology of WSNs intrusion tolerance issues. Through analyzing the power law index of the network invulnerability and topology of fault tolerance, the results of BAIM in the topology of the network survivability maximize the optimal network topology. Simulation results show that BAIM can not only keep the scale-free network robust to random failures, and but also improve the scale-free network vulnerability against intentional attacks and prolong the network lifetime.

scholarly journals The Fractional Preferential Attachment Scale-Free Network Model

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 509
Author(s):  
Rafał Rak ◽  
Ewa Rak
Keyword(s):  
Network Model ◽  
Real World ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Clustering Coefficient ◽  
Node Degree ◽  
Scale Free Network ◽  
Scale Free ◽  
Degree Correlation ◽  
Free Network

Many networks generated by nature have two generic properties: they are formed in the process of preferential attachment and they are scale-free. Considering these features, by interfering with mechanism of the preferential attachment, we propose a generalisation of the Barabási–Albert model—the ’Fractional Preferential Attachment’ (FPA) scale-free network model—that generates networks with time-independent degree distributions p ( k ) ∼ k − γ with degree exponent 2 < γ ≤ 3 (where γ = 3 corresponds to the typical value of the BA model). In the FPA model, the element controlling the network properties is the f parameter, where f ∈ ( 0 , 1 ⟩ . Depending on the different values of f parameter, we study the statistical properties of the numerically generated networks. We investigate the topological properties of FPA networks such as degree distribution, degree correlation (network assortativity), clustering coefficient, average node degree, network diameter, average shortest path length and features of fractality. We compare the obtained values with the results for various synthetic and real-world networks. It is found that, depending on f, the FPA model generates networks with parameters similar to the real-world networks. Furthermore, it is shown that f parameter has a significant impact on, among others, degree distribution and degree correlation of generated networks. Therefore, the FPA scale-free network model can be an interesting alternative to existing network models. In addition, it turns out that, regardless of the value of f, FPA networks are not fractal.

Research on cascading failure in multilayer network with different coupling preference

2017 ◽  
Vol 28 (04) ◽  
pp. 1750050 ◽  
Author(s):  
Yong Zhang ◽  
Lei Jin ◽  
Xiao Juan Wang
Keyword(s):  
Load Distribution ◽  
Random Network ◽  
Network Robustness ◽  
Cascading Failure ◽  
Failure Model ◽  
Scale Free Network ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Scale Free ◽  
Free Network

This paper is aimed at constructing robust multilayer networks against cascading failure. Considering link protection strategies in reality, we design a cascading failure model based on load distribution and extend it to multilayer. We use the cascading failure model to deduce the scale of the largest connected component after cascading failure, from which we can find that the performance of four kinds of load distribution strategies associates with the load ratio of the current edge to its adjacent edge. Coupling preference is a typical characteristic in multilayer networks which corresponds to the network robustness. The coupling preference of multilayer networks is divided into two forms: the coupling preference in layers and the coupling preference between layers. To analyze the relationship between the coupling preference and the multilayer network robustness, we design a construction algorithm to generate multilayer networks with different coupling preferences. Simulation results show that the load distribution based on the node betweenness performs the best. When the coupling coefficient in layers is zero, the scale-free network is the most robust. In the random network, the assortative coupling in layers is more robust than the disassortative coupling. For the coupling preference between layers, the assortative coupling between layers is more robust than the disassortative coupling both in the scale free network and the random network.

Research on new evolution model of scale-free network

2009 ◽  
Vol 29 (5) ◽  
pp. 1230-1232
Author(s):  
Hao RAO ◽  
Chun YANG ◽  
Shao-hua TAO
Keyword(s):  
Evolution Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network
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