Scalable Triangle Discovery Algorithm for Large Scale-Free Network with Limited Internal Memory

2020 ◽  
Vol 6 (4) ◽  
pp. 757-769 ◽  
Author(s):  
Xiaoping Zhou ◽  
Xun Liang ◽  
Zhi Tang
Keyword(s):  
Large Scale ◽  
Scale Free Network ◽  
Scale Free ◽  
Internal Memory ◽  
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.

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

A New Energy-Efficient and Fault-Tolerant Evolution Model for Large-Scale Wireless Sensor Networks Based on Complex Network Theory

2019 ◽  
Vol 10 (3) ◽  
pp. 21-36 ◽  
Author(s):  
Xiaobo Tan ◽  
Ji Tang ◽  
Liting Yu ◽  
Jialu Wang
Keyword(s):  
Network Theory ◽  
Energy Efficient ◽  
Large Scale ◽  
Fault Tolerant ◽  
Evolution Model ◽  
Scale Free Network ◽  
Complex Network Theory ◽  
Network Characteristics ◽  
Scale Free ◽  
Free Network

In this article, the authors present a new novel energy-efficient and fault-tolerant evolution model for large-scale wireless sensor networks based on complex network theory. In the evolution model, not only is the residual energy of each node considered, but also the constraint of links is introduced, which makes the energy consumption of the whole network more balanced. Furthermore, both preferential attachment and random attachment to the evolution model are introduced, which reduces the proportion of the nodes with high degree while keeping scale-free network characteristics to some extent. Theoretical analysis shows that the new model is an extension of the BA model, which is a mixed model between a BA model and a stochastic model. Simulation results show that EFEM has better stochastic network characteristics while keeping scale-free network characteristics if the value of random probability is near 0.2 and it can help to construct a high survivability network for large-scale WSNs.

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.

Pinning Control of Enterprise Marketing Network Based on Complex Network Theory

2014 ◽  
Vol 533 ◽  
pp. 491-494
Author(s):  
Xiao Hong Qin ◽  
Wei Ge
Keyword(s):  
Network Topology ◽  
Control Strategy ◽  
Network Theory ◽  
Large Scale ◽  
Pinning Control ◽  
Scale Free Network ◽  
Complex Network Theory ◽  
Scale Free ◽  
Free Network ◽  
Marketing Network

Complex enterprise marketing network is typical scale-free network. Based on the non-uniformity of enterprise marketing network topology, we select pinning control strategy. There are network members for selection a certain percentage to exercise control, to use of virtual control action associated with the coupling between network members, making large-scale enterprise marketing network reaches a steady state. Paper showed that pinning control is effectiveness to control of enterprise marketing for network.

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