Literature DB >> 15169068

Statistical analysis of airport network of China.

W Li1, X Cai.   

Abstract

Through the study of airport network of China (ANC), composed of 128 airports (nodes) and 1165 flights (edges), we show the topological structure of ANC conveys two characteristics of small worlds, a short average path length (2.067) and a high degree of clustering (0.733). The cumulative degree distributions of both directed and undirected ANC obey two-regime power laws with different exponents, i.e., the so-called double Pareto law. In-degrees and out-degrees of each airport have positive correlations, whereas the undirected degrees of adjacent airports have significant linear anticorrelations. It is demonstrated both weekly and daily cumulative distributions of flight weights (frequencies) of ANC have power-law tails. Besides, the weight of any given flight is proportional to the degrees of both airports at the two ends of that flight. It is also shown the diameter of each subcluster (consisting of an airport and all those airports to which it is linked) is inversely proportional to its density of connectivity. Efficiency of ANC and of its subclusters is measured through a simple definition. In terms of that, the efficiency of ANC's subclusters increases as the density of connectivity does. ANC is found to have an efficiency of 0.484.

Year:  2004        PMID: 15169068     DOI: 10.1103/PhysRevE.69.046106

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  19 in total

1.  Microdynamics in stationary complex networks.

Authors:  Aurelien Gautreau; Alain Barrat; Marc Barthélemy
Journal:  Proc Natl Acad Sci U S A       Date:  2009-05-19       Impact factor: 11.205

2.  Classes of complex networks defined by role-to-role connectivity profiles.

Authors:  Roger Guimerà; Marta Sales-Pardo; Luís A N Amaral
Journal:  Nat Phys       Date:  2007       Impact factor: 20.034

3.  Weight prediction in complex networks based on neighbor set.

Authors:  Boyao Zhu; Yongxiang Xia; Xue-Jun Zhang
Journal:  Sci Rep       Date:  2016-12-01       Impact factor: 4.379

4.  Structural and spectral properties of generative models for synthetic multilayer air transportation networks.

Authors:  Marzena Fügenschuh; Ralucca Gera; José Antonio Méndez-Bermúdez; Andrea Tagarelli
Journal:  PLoS One       Date:  2021-10-21       Impact factor: 3.240

5.  Forecasting the evolution of fast-changing transportation networks using machine learning.

Authors:  Weihua Lei; Luiz G A Alves; Luís A Nunes Amaral
Journal:  Nat Commun       Date:  2022-07-22       Impact factor: 17.694

6.  The immune-body cytokine network defines a social architecture of cell interactions.

Authors:  Ziv Frankenstein; Uri Alon; Irun R Cohen
Journal:  Biol Direct       Date:  2006-10-24       Impact factor: 4.540

7.  Critical cooperation range to improve spatial network robustness.

Authors:  Vitor H P Louzada; Nuno A M Araújo; Trivik Verma; Fabio Daolio; Hans J Herrmann; Marco Tomassini
Journal:  PLoS One       Date:  2015-03-20       Impact factor: 3.240

8.  Systemic delay propagation in the US airport network.

Authors:  Pablo Fleurquin; José J Ramasco; Victor M Eguiluz
Journal:  Sci Rep       Date:  2013-01-29       Impact factor: 4.379

9.  Graph theoretical analysis of complex networks in the brain.

Authors:  Cornelis J Stam; Jaap C Reijneveld
Journal:  Nonlinear Biomed Phys       Date:  2007-07-05

10.  Multi-scale analysis of the European airspace using network community detection.

Authors:  Gérald Gurtner; Stefania Vitali; Marco Cipolla; Fabrizio Lillo; Rosario Nunzio Mantegna; Salvatore Miccichè; Simone Pozzi
Journal:  PLoS One       Date:  2014-05-08       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.