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.

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 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.

Hub Agents Determine Collective Behavior

2015 ◽  
Vol 11 (02) ◽  
pp. 165-181
Author(s):  
Saori Iwanaga ◽  
Akira Namatame
Keyword(s):  
Social Network ◽  
Collective Behavior ◽  
Traffic Congestion ◽  
Scale Free Network ◽  
Collective Phenomena ◽  
Computing Technology ◽  
Scale Free ◽  
Cooperative Relationships ◽  
The Social ◽  
Free Network

There are growing interests for studying collective behavior including the dynamics of markets, the emergence of social norms and conventions and collective phenomena in daily life such as traffic congestion. In our previous work [Iwanaga and Namatame, Collective behavior and diverse social network, International Journal of Advancements in Computing Technology 4(22) (2012) 321–320], we showed that collective behavior in cooperative relationships is affected in the structure of the social network, the initial collective behavior and diversity of payoff parameter. In this paper, we focus on scale-free network and investigate the effect of number of interactions on collective behavior. And we found that choices of hub agents determine collective behavior.

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.

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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