Routing in Large Scale-Free Network with Community Structure

2009 ◽  
Author(s):  
Shi Wei ◽  
Zhao Zheng
Keyword(s):  
Community Structure ◽  
Large Scale ◽  
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 Evaluating the Impact of Name Resolution Dependence on the DNS

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Haiyan Xu ◽  
Zhaoxin Zhang ◽  
Jianen Yan ◽  
Xin Ma
Keyword(s):  
Large Scale ◽  
Scale Free Network ◽  
Security Measures ◽  
Scale Free ◽  
Domain Names ◽  
The Core ◽  
Complex Dependence ◽  
Random Failure ◽  
Free Network ◽  
The Impact

In the process of resolving domain names to IP addresses, there exist complex dependence relationships between domains and name servers. This paper studies the impact of the resolution dependence on the DNS through constructing a domain name resolution network based on large-scale actual data. The core nodes of the resolution network are mined from different perspectives by means of four methods. Then, both core attacks and random attacks on the network are simulated for further vulnerability analysis. The experimental results show that when the top 1% of the core nodes in the network are attacked, 46.19% of the domain names become unresolved, and the load of the residual network increases by nearly 195%, while only 0.01% of domain names fail to be resolved and the load increases with 18% in the same attack scale of the random mode. For these key nodes, we need to take effective security measures to prevent them from being attacked. The simulation experiment also proves that the resolution network is a scale-free network, which exhibits robustness against random failure and vulnerability against intentional attacks. These findings provide new references for the configuration of the DNS.

Toward Energy-Efficient and Robust Large-Scale WSNs: A Scale-Free Network Approach

2016 ◽  
Vol 34 (12) ◽  
pp. 4035-4047 ◽  
Author(s):  
Haixia Peng ◽  
Shuaizong Si ◽  
Mohamad Khattar Awad ◽  
Ning Zhang ◽  
Hai Zhao ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Large Scale ◽  
Network Approach ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

A FAST ESTIMATION ALGORITHM OF COMMUNITY NUMBER IN LARGE SCALE-FREE COMPLEX NETWORKS

2014 ◽  
Vol 28 (04) ◽  
pp. 1450039 ◽  
Author(s):  
PEIHUA FU ◽  
SHAN'AN ZHU ◽  
ANDING ZHU ◽  
XIAO DONG
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Optimal Parameter ◽  
Estimation Algorithm ◽  
Large Degree ◽  
Terminal Condition ◽  
Scale Free Network ◽  
Scale Free ◽  
Core Node ◽  
Free Network

In conventional community detecting algorithms, the community number is always a bypass product and cannot be estimated before partitioning. Since partitioning large scale and dynamic complex networks takes exhausting computation, the community number sometimes can be a terminal condition of iterations or a preset optimal parameter for speeding up partitioning algorithms. This paper assumes that communities are organized around the center of core nodes in a scale-free network. A separability function is built to dichotomize nodes into two classes and the class of large degree nodes is selected as the core node candidate set. An improved shortest path seeking algorithm is applied to remove the closest neighbors of a specific core node. The number of remaining core nodes is then the estimated number of communities. Experiments of real world scale-free networks and computer generated networks show that the results are very close to the well-proven results.

A stochastic SIR epidemic on scale-free network with community structure

2013 ◽  
Vol 392 (4) ◽  
pp. 974-981 ◽  
Author(s):  
Huiling Zhang ◽  
Zhi-Hong Guan ◽  
Tao Li ◽  
Xian-He Zhang ◽  
Ding-Xue Zhang
Keyword(s):  
Community Structure ◽  
Scale Free Network ◽  
Scale Free ◽  
Sir Epidemic ◽  
Free Network ◽  
Stochastic Sir

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
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