Distance Distribution between Complex Network Nodes in Hyperbolic Space

2016 ◽  
Vol 25 (3) ◽  
pp. 223-236 ◽  
Author(s):  
Gregorio Alanis-Lobato ◽  
Miguel A. Andrade-Navarro ◽  
Keyword(s):  
Complex Network ◽  
Hyperbolic Space ◽  
Distance Distribution ◽  
Network Nodes

scholarly journals Power-law distribution of degree–degree distance: A better representation of the scale-free property of complex networks

2020 ◽  
Vol 117 (26) ◽  
pp. 14812-14818 ◽  
Author(s):  
Bin Zhou ◽  
Xiangyi Meng ◽  
H. Eugene Stanley
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Power Law ◽  
Real World ◽  
Preferential Attachment ◽  
Statistical Tests ◽  
Finite Size ◽  
Distance Distribution ◽  
Scale Free ◽  
Degree Distance

Whether real-world complex networks are scale free or not has long been controversial. Recently, in Broido and Clauset [A. D. Broido, A. Clauset,Nat. Commun.10, 1017 (2019)], it was claimed that the degree distributions of real-world networks are rarely power law under statistical tests. Here, we attempt to address this issue by defining a fundamental property possessed by each link, the degree–degree distance, the distribution of which also shows signs of being power law by our empirical study. Surprisingly, although full-range statistical tests show that degree distributions are not often power law in real-world networks, we find that in more than half of the cases the degree–degree distance distributions can still be described by power laws. To explain these findings, we introduce a bidirectional preferential selection model where the link configuration is a randomly weighted, two-way selection process. The model does not always produce solid power-law distributions but predicts that the degree–degree distance distribution exhibits stronger power-law behavior than the degree distribution of a finite-size network, especially when the network is dense. We test the strength of our model and its predictive power by examining how real-world networks evolve into an overly dense stage and how the corresponding distributions change. We propose that being scale free is a property of a complex network that should be determined by its underlying mechanism (e.g., preferential attachment) rather than by apparent distribution statistics of finite size. We thus conclude that the degree–degree distance distribution better represents the scale-free property of a complex network.

scholarly journals Identifying Super-Spreader Nodes in Complex Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yu-Hsiang Fu ◽  
Chung-Yuan Huang ◽  
Chuen-Tsai Sun
Keyword(s):  
Infectious Diseases ◽  
Complex Networks ◽  
Complex Network ◽  
Information Dissemination ◽  
Local Features ◽  
Disease Detection ◽  
Network Nodes ◽  
Global Diversity ◽  
High Betweenness ◽  
Influential Individuals

Identifying the most influential individuals spreading information or infectious diseases can assist or hinder information dissemination, product exposure, and contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and highk-shell nodes have been identified as good initial spreaders, but efforts to use node diversity within network structures to measure spreading ability are few. Here we describe a two-step framework that combines global diversity and local features to identify the most influential network nodes. Results from susceptible-infected-recovered epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets.

INFORMATION ASYMMETRY FLOWING IN COMPLEX NETWORKS

2012 ◽  
Vol 26 (31) ◽  
pp. 1250183
Author(s):  
CHEN-XI SHAO ◽  
HUI-LING DOU ◽  
BING-HONG WANG
Keyword(s):  
Symmetry Breaking ◽  
Complex Networks ◽  
Information Asymmetry ◽  
Complex Network ◽  
Information Flow ◽  
Weak Link ◽  
Network Evolution ◽  
Long Tail ◽  
Network Nodes ◽  
Concept Of Information

The concept of information asymmetry in complex networks is introduced on the basis of information asymmetry in economics and symmetry breaking. Information flowing between two nodes on a link is bidirectional, whose size is closely related to traffic dynamics on the network. Based on asymmetric information theory, we proposed information flow between network nodes is asymmetrical. We designed two methods to calculate the amount of information flow based on two mechanisms of complex network. Unequal flow of two opposite directions on the same link proved information asymmetry exists in the complex network. A complex network evolution model based on symmetry breaking is established, which is a truthful example for complex network mimicking nature. The evolution mechanism of symmetry breaking can best explain the phenomenon of the weak link and long tail theory in complex network.

Distance Distribution of Bivariate Poisson Network Nodes

2010 ◽  
Vol 14 (9) ◽  
pp. 848-850 ◽  
Author(s):  
Alireza Babaei ◽  
Bijan Jabbari
Keyword(s):  
Distance Distribution ◽  
Network Nodes

scholarly journals Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network

2008 ◽  
Vol 387 (24) ◽  
pp. 6201-6214 ◽  
Author(s):  
Luciano da Fontoura Costa ◽  
Marilza A. Rodrigues Tognetti ◽  
Filipi Nascimento Silva
Keyword(s):  
Complex Network ◽  
Collaboration Network ◽  
Network Nodes ◽  
Institutional Collaboration

scholarly journals Urban Rail Transit System Network Reliability Analysis Based on a Coupled Map Lattice Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shaojie Wu ◽  
Yan Zhu ◽  
Ning Li ◽  
Yizeng Wang ◽  
Xingju Wang ◽  
...  
Keyword(s):  
Complex Network ◽  
Network Reliability ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Weighted Network ◽  
Network Nodes ◽  
Transit Network ◽  
Passenger Flow ◽  
Urban Rail ◽  
Passenger Flows

During the last twenty years, the complex network modeling approach has been introduced to assess the reliability of rail transit networks, in which the dynamic performance involving passenger flows have attracted more attentions during operation stages recently. This paper proposes the passenger-flow-weighted network reliability evaluation indexes, to assess the impact of passenger flows on network reliability. The reliability performances of the rail transit network and passenger-flow-weighted one are analyzed from the perspective of a complex network. The actual passenger flow weight of urban transit network nodes was obtained from the Shanghai Metro public transportation card data, which were used to assess the reliability of the passenger-flow-weighted network. Furthermore, the dynamic model of the Shanghai urban rail transit network was constructed based on the coupled map lattice (CML) model. Then, the processes of cascading failure caused by network nodes under different destructive situations were simulated, to measure the changes of passenger-flow-weighted network reliability during the processes. The results indicate that when the scale of network damage attains 50%, the reliability of the passenger-flow-weighted network approaches zero. Consequently, taking countermeasures during the initial stage of network cascading may effectively prevent the disturbances from spreading in the network. The results of the paper could provide guidelines for operation management, as well as identify the unreliable stations within passenger-flow-weighted networks.

HYBRID PINNING CONTROL FOR COMPLEX NETWORKS

2012 ◽  
Vol 22 (10) ◽  
pp. 1250252 ◽  
Author(s):  
LUIZ FELIPE R. TURCI ◽  
ELBERT E. N. MACAU
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Chaos Control ◽  
Dynamical Behavior ◽  
Pinning Control ◽  
Synchronization Control ◽  
Network Nodes

In this work, we present two different hybrid pinning strategies to synchronize a complex network of identical agents into a known desired solution. The first strategy is the chaos control hybrid pinning in which pinning synchronization control and chaos control are merged. The second strategy is the nonidentical reference hybrid pinning, in which the pinning reference dynamical behavior is different from network nodes dynamical behavior.

Logistics Network Nodes Importance Analysis Based on the Complex Network Theory

2013 ◽  
Vol 336-338 ◽  
pp. 2410-2414 ◽  
Author(s):  
Yina Wu ◽  
Hui Ma
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Small World ◽  
Logistics Industry ◽  
Network Nodes ◽  
Complex Network Theory ◽  
Logistics Network ◽  
Logistics Systems ◽  
Importance Analysis ◽  
Development And Management

Logistics systems can be abstracted to complex networks which are composed of logistics nodes and transport routes. The structure and geometric properties of the complex network has an important impact on the logistics industry development and management. The article use a provinces logistics network as the prototype and build a complex network. Apply complex network theory to analyze the logistics network. The article found that the logistics network has small-world properties. Also, the article discussed the important nodes based on the statistical indictors. Finally, compare the results with the real planning nodes level.

Critical airports of the world air sector network based on the centrality and entropy theory

2021 ◽  
pp. 2150081
Author(s):  
Yun Wang ◽  
Kou Meng ◽  
Huidong Wu ◽  
Jun Hu ◽  
Peng Wu
Keyword(s):  
Complex Network ◽  
Location Information ◽  
Entropy Theory ◽  
Entropy Weight ◽  
Network Nodes ◽  
The World ◽  
Evaluation Matrix ◽  
Better Than

In this paper, an air sector network (ASN) is built based on the obtained data describing the airspace of world. In the constructed network, nodes and edges represents the airports and airlines respectively. Based on the complex network and the entropy, we find that the airport rank obtained through the centrality and entropy theory is better than that derived through the traditional means. The improved entropy weight algorithm is used to reflect the location information of the nodes in the network, synthesize the centrality of the previous complex network, construct the importance evaluation matrix and finally calculate the ranking results of the importance of the nodes. Finally, through calculation, we found that the ranking result after comprehensive consideration is better than that being determined only through centrality, which illustrates the effectiveness of the method.

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