scholarly journals A quality-of-service-aware dynamic evolution model for space–ground integrated network

2017 ◽  
Vol 13 (8) ◽  
pp. 155014771772864 ◽  
Author(s):  
Zhuo Yi ◽  
Xuehui Du ◽  
Ying Liao ◽  
Lifeng Cao
Keyword(s):  
Quality Of Service ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Small World ◽  
Evolution Model ◽  
Dynamic Evolution ◽  
Power Law Distribution ◽  
Integrated Network ◽  
Complex Network Theory

Space–ground integrated network, a strategic, driving, and irreplaceable infrastructure, guarantees the development of economic and national security. However, its natures of limited resources, frequent handovers, and intermittently connected links significantly reduce the quality of service. To address this issue, a quality-of-service-aware dynamic evolution model is proposed based on complex network theory. On one hand, a quality-of-service-aware strategy is adopted in the model. During evolution phases of growth and handovers, links are established or deleted according to the quality-of-service-aware preferential attachment following the rule of better quality of service getting richer and worse quality of service getting poor or to die. On the other hand, dynamic handover of nodes and intermittent connection of links are taken into account and introduced into the model. Meanwhile, node heterogeneity is analyzed and heterogeneous nodes are endowed with discriminate interactions. Theoretical analysis and simulations are utilized to explore the degree distribution and its characteristics. Results reveal that this model is a scale-free model with drift power-law distribution, fat-tail and small-world effect, and drift character of degree distribution results from dynamic handover. Furthermore, this model exerts well fault tolerance and attack resistance compared to signal-strength-based strategy. In addition, node heterogeneity and quality-of-service-aware strategy improve the attack resistance and overall quality of service of space–ground integrated network.

scholarly journals The structure of co-publications multilayer network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ghislain Romaric Meleu ◽  
Paulin Yonta Melatagia
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Small World ◽  
Generation Model ◽  
Small World Network ◽  
Power Law Distribution ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Scientific Papers

AbstractUsing the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.

Research on the Structure of Peer-to-Peer Overlay Based on the Complex Network Theory

2012 ◽  
Vol 263-266 ◽  
pp. 1096-1099
Author(s):  
Zhi Yong Jiang
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Degree Distribution ◽  
Path Length ◽  
Small World ◽  
Peer To Peer ◽  
Clustering Coefficient ◽  
Power Law Distribution ◽  
Complex Network Theory ◽  
The Mean

Relationship between nodes in peer-to-peer overlay, currently becomes a hot topic in the field of complex network. In this paper a model of peer-to-peer overlay was purposed. And then the paper focused on figuring out the mean-shortest path length (MSPL), clustering coefficient (CC) and the degree of every node which allowed us to discover the degree distribution. The results show that the degree distribution function follows approximately power law distribution and the network possesses notable clustering and small-world properties.

scholarly journals An Approach for Understanding and Promoting Coal Mine Safety by Exploring Coal Mine Risk Network

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Yongliang Deng ◽  
Liangliang Song ◽  
Zhipeng Zhou ◽  
Ping Liu
Keyword(s):  
Coal Mine ◽  
Path Length ◽  
Risk Control ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Mine Safety ◽  
Power Law Distribution ◽  
Complex Network Theory ◽  
Scale Free

Capturing the interrelations among risks is essential to thoroughly understand and promote coal mining safety. From this standpoint, 105 risks and 135 interrelations among risks had been identified from 126 typical accidents, which were also the foundation of constructing coal mine risk network (CMRN). Based on the complex network theory and Pajek, six parameters (i.e., network diameter, network density, average path length, degree, betweenness, and clustering coefficient) were employed to reveal the topological properties of CMRN. As indicated by the results, CMRN possesses scale-free network property because its cumulative degree distribution obeys power-law distribution. This means that CMRN is robust to random hazard and vulnerable to deliberate attack. CMRN is also a small-world network due to its relatively small average path length as well as high clustering coefficient, implying that accident propagation in CMRN is faster than regular network. Furthermore, the effect of risk control is explored. According to the result, it shows that roof collapse, fire, and gas concentration exceeding limit refer to three most valuable targets for risk control among all the risks. This study will help offer recommendations and proposals for making beforehand strategies that can restrain original risks and reduce accidents.

scholarly journals Formation of Chinese venture capital syndication network

2020 ◽  
Vol 72 (1) ◽  
pp. 49-64 ◽  
Author(s):  
Makoto Nirei ◽  
Toshiaki Shoji ◽  
Fei Yu
Keyword(s):  
Venture Capital ◽  
Power Law ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Statistical Properties ◽  
Power Law Distribution

AbstractUsing a dataset that recorded a large number of investment transactions in China from 1991 to 2018, we examine the statistical properties of the Chinese venture capital (VC) syndication network. Our main findings are as follows. First, the number of investment transactions sharply increased after 2014. Second, more than half of the VC firms are located in Beijing, Shanghai, and Shenzhen. Third, the degree distribution becomes roughly straight on a log–log plot. Fourth, the hypothesis that the degree distribution follows a power-law distribution is not rejected for 2015 and 2016. Fifth, better connected VC firms increase their connectivity faster, which suggests the existence of preferential attachment.

PREFERENTIAL ATTACHMENT, AGING AND WEIGHTS IN RECOMMENDATION SYSTEMS

2009 ◽  
Vol 19 (02) ◽  
pp. 755-763 ◽  
Author(s):  
MASSIMILIANO ZANIN ◽  
PEDRO CANO ◽  
OSCAR CELMA ◽  
JAVIER M. BULDÚ
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Recommendation System ◽  
Preferential Attachment ◽  
Recommendation Systems ◽  
Complex Network Theory ◽  
Underlying Principle ◽  
Exact Moment ◽  
User Ratings

In the present work, algorithms based on complex network theory are applied to Recommendation Systems in order to improve their quality of predictions. We show how some networks are grown under the influence of trendy forces, and how this can be used to enhance the results of a recommendation system, i.e. increase their percentage of right predictions. After defining a base algorithm, we create recommendation networks which are based on a histogram of user ratings, using therefore an underlying principle of preferential attachment. We show the influence of data aging in the prediction of user habits and how the exact moment of the prediction influences the recommendation. Finally, we design weighted networks that take into account the age of the information used to generate the links. In this way, we obtain a better approximation to evaluate the users' tastes.

scholarly journals MODELLING COLLABORATION NETWORKS BASED ON NONLINEAR PREFERENTIAL ATTACHMENT

2007 ◽  
Vol 18 (02) ◽  
pp. 297-314 ◽  
Author(s):  
TAO ZHOU ◽  
BING-HONG WANG ◽  
YING-DI JIN ◽  
DA-REN HE ◽  
PEI-PEI ZHANG ◽  
...  
Keyword(s):  
Empirical Data ◽  
Degree Distribution ◽  
Alternative Model ◽  
Free Parameter ◽  
Preferential Attachment ◽  
Average Distance ◽  
Small World ◽  
Clustering Coefficient ◽  
Collaboration Networks ◽  
Scale Free

In this paper, we propose an alternative model for collaboration networks based on nonlinear preferential attachment. Depending on a single free parameter "preferential exponent", this model interpolates between networks with a scale-free and an exponential degree distribution. The degree distribution in the present networks can be roughly classified into four patterns, all of which are observed in empirical data. And this model exhibits small-world effect, which means the corresponding networks are of very short average distance and highly large clustering coefficient. More interesting, we find a peak distribution of act-size from empirical data which has not been emphasized before. Our model can produce the peak act-size distribution naturally that agrees with the empirical data well.

Community Structure Analysis in Social Network of Sina Weibo

2014 ◽  
Vol 590 ◽  
pp. 756-762 ◽  
Author(s):  
Sheng Wu Xu ◽  
Xia Zhang ◽  
Zheng You Xia
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Structure Analysis ◽  
Degree Distribution ◽  
Structural Characteristics ◽  
Online Social Network ◽  
Small World ◽  
Directed Network ◽  
Power Law Distribution ◽  
On Line

Online social network is different from the traditional social network. it has distinctive characteristics, such as openness, anonymity, across the region, a high degree of interactivity and complexity. In this paper, community structure analysis in social network of Sina Weblog is analyzed and discussed based on directed network. According to the characteristics of Sina Weblog, We first constructed three kinds of network (such as follow-network, fan-network and all-network) and discussed degree distribution of weibo users. Then, structure and characteristics of Sina Weblog social network community is discussed and analyze based on the three network types on the. The experiments show that part and integral has the same properties, Degree distribution obeys power-law distribution, community has a small world and users are in accordance with six degrees of separation theory in Sina Weblog community. These research results verify that Sina Weblog has the structural characteristics of on line social relation network.

scholarly journals A Topology Evolution Model Based on Revised PageRank Algorithm and Node Importance for Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaogang Qi ◽  
Lifang Liu ◽  
Guoyong Cai ◽  
Mande Xie
Keyword(s):  
Theoretical Analysis ◽  
Network Topology ◽  
Preferential Attachment ◽  
Research Field ◽  
Evolution Model ◽  
Clustering Coefficient ◽  
Wireless Sensor ◽  
Power Law Distribution ◽  
Pagerank Algorithm ◽  
Node Importance

Wireless sensor network (WSN) is a classical self-organizing communication network, and its topology evolution currently becomes one of the attractive issues in this research field. Accordingly, the problem is divided into two subproblems: one is to design a new preferential attachment method and the other is to analyze the dynamics of the network topology evolution. To solve the first subproblem, a revised PageRank algorithm, called Con-rank, is proposed to evaluate the node importance upon the existing node contraction, and then a novel preferential attachment is designed based on the node importance calculated by the proposed Con-rank algorithm. To solve the second one, we firstly analyze the network topology evolution dynamics in a theoretical way and then simulate the evolution process. Theoretical analysis proves that the network topology evolution of our model agrees with power-law distribution, and simulation results are well consistent with our conclusions obtained from the theoretical analysis and simultaneously show that our topology evolution model is superior to the classic BA model in the average path length and the clustering coefficient, and the network topology is more robust and can tolerate the random attacks.

scholarly journals Random complex networks

2014 ◽  
Vol 1 (3) ◽  
pp. 357-367 ◽  
Author(s):  
Michael Small ◽  
Lvlin Hou ◽  
Linjun Zhang
Keyword(s):  
Complex Networks ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Small World ◽  
Node Degree ◽  
Scale Free ◽  
Wide Range ◽  
Random Complex ◽  
Law Degree ◽  
Attachment Process

Abstract Exactly what is meant by a ‘complex’ network is not clear; however, what is clear is that it is something other than a random graph. Complex networks arise in a wide range of real social, technological and physical systems. In all cases, the most basic categorization of these graphs is their node degree distribution. Particular groups of complex networks may exhibit additional interesting features, including the so-called small-world effect or being scale-free. There are many algorithms with which one may generate networks with particular degree distributions (perhaps the most famous of which is preferential attachment). In this paper, we address what it means to randomly choose a network from the class of networks with a particular degree distribution, and in doing so we show that the networks one gets from the preferential attachment process are actually highly pathological. Certain properties (including robustness and fragility) which have been attributed to the (scale-free) degree distribution are actually more intimately related to the preferential attachment growth mechanism. We focus here on scale-free networks with power-law degree sequences—but our methods and results are perfectly generic.

scholarly journals Using Network Theory to Explore BIM Application Barriers for BIM Sustainable Development in China

2020 ◽  
Vol 12 (8) ◽  
pp. 3190
Author(s):  
Yongliang Deng ◽  
Jinyun Li ◽  
Qiuting Wu ◽  
Shuangshuang Pei ◽  
Na Xu ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Network Theory ◽  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Information Modeling ◽  
Power Law Distribution ◽  
Complex Network Theory ◽  
Scale Free

Building Information Modeling (BIM) technology has promoted the development of the architecture, engineering, and construction (AEC) industry, but has encountered many barriers to its application in China. Therefore, identifying the barriers to BIM application and capturing their interactions are essential in order to control and eliminate the determined barriers. From this standpoint, 23 BIM application barriers were identified through a literature review and expert interviews. Furthermore, the interactions among them were determined based on the Delphi method, which was the foundation for establishing the BIM application barrier network (BABN). Then, the software Pajek was employed to construct the network model and reveal its topological characteristics based on complex network theory, including degree, betweenness, eigenvector, clustering coefficient, network diameter, and average path length. As indicated by the results, BABN possesses scale-free network property because its cumulative degree distribution obeys power–law distribution. BABN is also a small-world network, due to its relatively high clustering coefficient as well as small average path length, implying that barrier propagation in BABN is fast. In addition, the results are discussed and recommendations are proposed. This research will help BIM stakeholders to develop coping strategies to control and eliminate BIM application barriers for the sake of driving BIM sustainable development.

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