计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 321-325.doi: 10.11896/jsjkx.200900112

• 智能计算 • 上一篇    下一篇

基于复杂网络的全球航空网络结构分析与应用

胡军1, 王雨桐2, 何欣蔚2, 武晖栋2, 李慧嘉3   

  1. 1 福州大学经济与管理学院 福州350108
    2 中央财经大学管理科学与工程学院 北京100081
    3 北京邮电大学理学院 北京100876
  • 出版日期:2021-06-10 发布日期:2021-06-17
  • 通讯作者: 李慧嘉(hjli@amss.cn)
  • 作者简介:jungehu@126.com
  • 基金资助:
    国家自然科学面上基金(71871233,71701049,717871159);北京邮电大学提升科技创新能力行动计划项目(2020XD-A01-1)

Analysis and Application of Global Aviation Network Structure Based on Complex Network

HU Jun1, WANG Yu-tong2, HE Xin-wei2, WU Hui-dong2, LI Hui-jia3   

  1. 1 School of Economics and Management,Fuzhou University,Fuzhou 350108,China
    2 School of Management Science and Engineering,Central University of Finance and Economics,Beijing 100081,China
    3 School of Science,Beijing University of Posts and Telecommunications,Beijing 100876,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:HU Jun,born in 1993,Ph.D candidate.His main research interests include complex network,cluster,social network,and control theory.
    LI Hui-jia,born in 1985,Ph.D,professor.His main research interests include data mining,pattern recognition,complex network,and control theory.
  • Supported by:
    National Natural Science Foundation of China(71871233,71701049,717871159) and Fundamental Research Funds for the Central Universities of China (2020XD-A01-1).

摘要: 随着国内和国际间贸易活动规模的不断扩大,航空运输的经济价值和社会价值不断提高。飞机为航空运输实现的载体,构建出航空网络并对其结构进行实证研究与分析有着极其重要的现实意义。文中主要以全球航班信息为基础,借助复杂网络对全球航空网络进行分析,发现全球航空网络是一个无标度的小世界网络,其度分布为幂律分布。通过拟合发现点介数与度主要呈指数关系,但是在随着度的增加,点介数与度之间主要为线性关系,并且随着度的增加聚类系数也趋于稳定。此外,通过社团划分算法发现,全球航空网络具有明显的区域聚集效应。

关键词: 复杂网络, 航空网络, 社团划分, 小世界网络

Abstract: With the continuous expansion of domestic and international trade activities,the economic and social value of air transportation are constantly improved.As the carrier of air transport,the empirical study and analysis of aviation network is of great significance.Based on the global flight information,this paper analyzes the global aviation network with the help of complex network,and finds that the global aviation network is a scale-free small-world network,whose degree distribution is power law distribution.Through fitting,it is found that the number of points between the number and degree is mainly exponential,but with the increase of degree,the number of points between the number and degree is mainly linear,and the clustering coefficient tends to be stable with the increase of degree.In addition,this paper finds that the global aviation network has obvious regional clustering effect through the community partition algorithm.

Key words: Aviation network, Community division, Complex network, Small world network

中图分类号: 

  • TP393
[1] BARRAT A,BARTHELEMY M,VWSPIGNANI A.Theeffects of spatial constraints on the evolution of weighted complex networks[J].Journal of Statistical Mechanics:Theory and Experiment,2005(5):P05003.
[2] LI W,CAI X.Statistical analysis ofairport network of China[J].Physical Rwview E,2004,69(4pt2):046106.
[3] WANG R,CAI X.Hierarchical structure,disassortativity and in-formation measures of the US flight network[J].Chin.Phys.Lett.,2005,22(10):2715-2718.
[4] WANG J E,MO H H,JIN F G.Spatial Structural characteristics of Chinese aviation network based on complex network theory[J].Acta Geograhical Sinica,2009,64(8):899-910.
[5] 曾小舟,江可申,程凯.我国航空网络枢纽机场中心化水平比较分析[J].系统工程,2010,28(9):39-45.
[6] WATTS D J,STROGATZ S H.Collective dynamics of “small-world” networks [J].Nature,1998,1393:440-442.
[7] NEWMAN M E J.Models of the small world:a review [J].J.Stat.Phys.,2000,101:819-840.
[8] STROGATZ S H.Exploring complex networks[J].Nature,2001,410(6825):268-276.
[9] 陈航宇,李慧嘉.中国航空复杂网络的结构特征与应用分析[J].计算机科学,2019(6):300-304.
[10] 曾小舟,唐笑笑,江可申.基于复杂网络理论的中国航空网络结构实证研究[J].交通运输系统工程与信息,2011,11(6):175-181.
[11] 王俊超,殷志远,冯光柳.复杂网络特性及可靠性分析-以中国航空网为例[J].微型电脑应用,2013,29(11):13-16.
[12] 杨卓璇,马源培,李慧嘉.基于DEA模型的中国水行业上市企业的效率和业务类型关系研究[J].聊城大学学报(自然科学版),2020,33(6):12-26.
[13] 马源培,杨卓璇,李慧嘉.结合Bass模型和LTV的创新产品扩散预测[J].聊城大学学报(自然科学版),2020,33(4):26-32.
[14] RÉKA A,BARABÁSI A L.Statistical mechanics of complexnetworks [J].Reviews of Modern Physics,2002,74(1):47.
[15] ROMUALDO P S,VÁZQUEZ A,VESPIGNANI A.Dynamical and correlation properties of the Internet [J].Physical Review Letters,2001,87(25):258701.
[16] PODANI J,OLTVAI Z N,JEONG H,et al.Comparable system-level organization of Archaea and Eukaryotes [J].Nature Genetics,2001,29(1):54-56.
[17] LEON D,DÍAZ-GUILERA A,ARENAS A.The effect of size heterogeneity on community identification in complex networks[J].Journal of Statistical Mechanics:Theory and Experiment,2006,2006,11:P11010.
[18] FORTUNATO,SANTO.Community detection in graphs[J].Physics Reports,2010,486(3):75-174.
[19] GIRVAN M,NEWMAN M E J.Community structure in social and biological networks [J].Proceedings of the National Academy of Sciences,2002,99(12):7821-782.
[1] 郑文萍, 刘美麟, 杨贵.
一种基于节点稳定性和邻域相似性的社区发现算法
Community Detection Algorithm Based on Node Stability and Neighbor Similarity
计算机科学, 2022, 49(9): 83-91. https://doi.org/10.11896/jsjkx.220400146
[2] 何茜, 贺可太, 王金山, 林绅文, 杨菁林, 冯玉超.
比特币实体交易模式分析
Analysis of Bitcoin Entity Transaction Patterns
计算机科学, 2022, 49(6A): 502-507. https://doi.org/10.11896/jsjkx.210600178
[3] 杨波, 李远彪.
数据科学与大数据技术课程体系的复杂网络分析
Complex Network Analysis on Curriculum System of Data Science and Big Data Technology
计算机科学, 2022, 49(6A): 680-685. https://doi.org/10.11896/jsjkx.210800123
[4] 王本钰, 顾益军, 彭舒凡, 郑棣文.
融合动态距离和随机竞争学习的社区发现算法
Community Detection Algorithm Based on Dynamic Distance and Stochastic Competitive Learning
计算机科学, 2022, 49(5): 170-178. https://doi.org/10.11896/jsjkx.210300206
[5] 陈世聪, 袁得嵛, 黄淑华, 杨明.
基于结构深度网络嵌入模型的节点标签分类算法
Node Label Classification Algorithm Based on Structural Depth Network Embedding Model
计算机科学, 2022, 49(3): 105-112. https://doi.org/10.11896/jsjkx.201000177
[6] 赵学磊, 季新生, 刘树新, 李英乐, 李海涛.
基于路径连接强度的有向网络链路预测方法
Link Prediction Method for Directed Networks Based on Path Connection Strength
计算机科学, 2022, 49(2): 216-222. https://doi.org/10.11896/jsjkx.210100107
[7] 李家文, 郭炳晖, 杨小博, 郑志明.
基于信息传播的致病基因识别研究
Disease Genes Recognition Based on Information Propagation
计算机科学, 2022, 49(1): 264-270. https://doi.org/10.11896/jsjkx.201100129
[8] 穆俊芳, 郑文萍, 王杰, 梁吉业.
基于重连机制的复杂网络鲁棒性分析
Robustness Analysis of Complex Network Based on Rewiring Mechanism
计算机科学, 2021, 48(7): 130-136. https://doi.org/10.11896/jsjkx.201000108
[9] 刘汉卿, 康晓东, 高万春, 李博, 王亚鸽, 张华丽, 白放.
基于多模型的COVID-19传播研究
Research on Propagation of COVID-19 Based on Multiple Models
计算机科学, 2021, 48(6A): 196-202. https://doi.org/10.11896/jsjkx.201100086
[10] 王学光, 张爱新, 窦炳琳.
复杂网络上的非线性负载容量模型
Non-linear Load Capacity Model of Complex Networks
计算机科学, 2021, 48(6): 282-287. https://doi.org/10.11896/jsjkx.200700040
[11] 马媛媛, 韩华, 瞿倩倩.
基于节点亲密度的重要性评估算法
Importance Evaluation Algorithm Based on Node Intimate Degree
计算机科学, 2021, 48(5): 140-146. https://doi.org/10.11896/jsjkx.200300184
[12] 殷子樵, 郭炳晖, 马双鸽, 米志龙, 孙怡帆, 郑志明.
群智体系网络结构的自治调节:从生物调控网络结构谈起
Autonomous Structural Adjustment of Crowd Intelligence Network: Begin from Structure of Biological Regulatory Network
计算机科学, 2021, 48(5): 184-189. https://doi.org/10.11896/jsjkx.210200161
[13] 刘胜久, 李天瑞, 谢鹏, 刘佳.
带权图的多重分形度量
Measure for Multi-fractals of Weighted Graphs
计算机科学, 2021, 48(3): 136-143. https://doi.org/10.11896/jsjkx.200700159
[14] 龚追飞, 魏传佳.
基于改进AdaBoost算法的复杂网络链路预测
Link Prediction of Complex Network Based on Improved AdaBoost Algorithm
计算机科学, 2021, 48(3): 158-162. https://doi.org/10.11896/jsjkx.200600075
[15] 龚追飞, 魏传佳.
基于拓扑相似和XGBoost的复杂网络链路预测方法
Complex Network Link Prediction Method Based on Topology Similarity and XGBoost
计算机科学, 2021, 48(12): 226-230. https://doi.org/10.11896/jsjkx.200800026
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!