Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 300-304.

• Network & Communication • Previous Articles     Next Articles

Analysis of Characteristics and Applications of Chinese Aviation Complex Network Structure

CHEN Hang-yu, LI Hui-jia   

  1. School of Management Science and Engineering,Central University of Finance and Economics,Beijing 100081,China
  • Online:2019-06-14 Published:2019-07-02

Abstract: With the continuous improvement of the economic and social value of air transport,as the carrier of air transport,the research and analysis of air transport network structure is of great significance.Based on the flight data of major airlines in China,this paper used complex network theory to analyze the network characteristics of China’s aviation network,and proved that China’s aviation network is a small-world network with scale-free characteristics.By analyzing the basic statistical characteristics of China Aviation Complex Network in 2015,we found that the average path length decreases,the average degree of nodes increases,and the clustering coefficient tends to be stable.After that,the paper analyzed the interaction of node index,edge index and weighted index of China Aviation Complex Network,and studied the influence of different index changes on network structure and its practical significance.In addition,the paper found that the degree-degree correlation,degree-weight correlation and betweenness-betweenness correlation,which reflect the connection preference and structural characteristics of China Airline Network,are negative.Finally,the application analysis and prospect of the research results were carried out.

Key words: Chinese airline network, Complex network, Statistical characteristics, Empirical analysis, Power law distribution

CLC Number: 

  • TP393
[1] BARRAT A,BARTHELEMY M,VESPIGNANI A.The ef-fects 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 of airport network of China[J].Physical Review E,2004,69(4pt2),046106.
[3] WANG R,CAI X.Hierarchical structure,disassortativity and information measures of the US flight network[J].Chinese Physics Letters,2005,22(10):2715-2718.
[4] 王姣娥,莫辉辉,金凤君.中国航空网络空间结构的复杂性[J].地理学报,2009,64(8):899-910.
[5] 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.
[6] 崔博.中国民用航空网络的结构分析与空间集聚问题研究[D].南京:南京航空航天大学,2014.
[7] WATTS D J,STROGATZ S H.Collective dynamics of “small-word” networks[J].Nature,1998,393:440-442.
[8] NEWMAN M E J.Models of the small world:a review[J].Journal of Statistical Physical,2000,101:819-840.
[9] STROGATZ S H.Exploring complex networks[J].Nature, 2001,410(6825):268-276.
[10] 刘宏鲲,周涛.中国城市航空网络的实证研究与分析[J].物理学报,2007,56(1):106-112.
[11] NEWMAN M E J.Assortative mixing in networks[J].Physical Review Letters,2002,89,208701.
[12] 王姣娥,莫辉辉.中国航空网络演化过程的复杂性研究[J].交通运输系统工程与信息,2014(1):71-80.
[13] 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(11):13-16.
[14] 闫玲玲,陈增强,张青.基于度和聚类系数的中国航空网络重要性节点分析[J].智能系统学报,2016(5):586-593.
[15] ROCHA L E C.Dynamics of Air Transport Networks:A Review from a Complex Systems Perspective[J].Chinese Journal of Aeronautics,2017,30(2):469-478.
[16] 李想.中国航空网络效率的空间演化及其影响因素研究[D].上海:华东师范大学,2018.
[17] 冯慧芳,柏凤山,徐有基.基于轨迹大数据的城市交通感知和路网关键节点识别[J].交通运输系统工程与信息,2018(3):42-47.
[18] 邵峰晶,孙仁诚,隋毅,等.基于复杂网络的大数据分析[J].计算机仿真,2018(5):1-8.
[19] 窦梅,孙仁诚,葛兆富,等.公共交通档案大数据有效分析与利用研究[J].档案学研究,2018(4):59-61.
[1] YANG Chao, LIU Zhi. Study on Complex Network Cascading Failure Based on Totally Asymmetric Simple Exclusion Process Model [J]. Computer Science, 2020, 47(9): 265-269.
[2] ZHANG Meng-yue, HU Jun, YAN Guan, LI Hui-jia. Analysis of China’s Patent Application Concern Based on Visibility Graph Network [J]. Computer Science, 2020, 47(8): 189-194.
[3] ZHANG Qing-qi, LIU Man-dan. Multi-objective Five-elements Cycle Optimization Algorithm for Complex Network Community Discovery [J]. Computer Science, 2020, 47(8): 284-290.
[4] WANG Hui, LE Zi-chun, GONG Xuan, WU Yu-kun, ZUO Hao. Review of Link Prediction Methods Based on Feature Classification [J]. Computer Science, 2020, 47(8): 302-312.
[5] DONG Ming-gang, GONG Jia-ming and JING Chao. Multi-obJective Evolutionary Algorithm Based on Community Detection Spectral Clustering [J]. Computer Science, 2020, 47(6A): 461-466.
[6] YUAN Rong, SONG Yu-rong, MENG Fan-rong. Link Prediction Method Based on Weighted Network Topology Weight [J]. Computer Science, 2020, 47(5): 265-270.
[7] MA Yang, CHENG Guang-quan, LIANG Xing-xing, LI Yan, YANG Yu-ling, LIU Zhong. Improved SDNE in Weighted Directed Network [J]. Computer Science, 2020, 47(4): 233-237.
[8] ZHANG Hu, ZHOU Jing-jing, GAO Hai-hui, WANG Xin. Network Representation Learning Method on Fusing Node Structure and Content [J]. Computer Science, 2020, 47(12): 119-124.
[9] RUAN Zi-rui,RUAN Zhong-yuan,SHEN Guo-jiang. Study of TASEP Model Based on Road Networks [J]. Computer Science, 2020, 47(1): 265-269.
[10] ZHAO Lei, ZHOU Jin-he. ICN Energy Efficiency Optimization Strategy Based on Content Field of Complex Networks [J]. Computer Science, 2019, 46(9): 137-142.
[11] LIU Xiao-dong, WEI Hai-ping, CAO Yu. Modeling and Stability Analysis for SIRS Model with Network Topology Changes [J]. Computer Science, 2019, 46(6A): 375-379.
[12] ZHANG Sen, LIU Wen-qi, ZHAO Ning. Research of Consensus in Multi-agent Systems on Complex Network [J]. Computer Science, 2019, 46(4): 95-99.
[13] SHAN Na, LI Long-jie, LIU Yu-yang, CHEN Xiao-yun. Link Prediction Based on Correlation of Nodes’ Connecting Patterns [J]. Computer Science, 2019, 46(12): 20-25.
[14] BIN Sheng, SUN Geng-xin. Collaborative Filtering Recommendation Algorithm Based on Multi-relationship Social Network [J]. Computer Science, 2019, 46(12): 56-62.
[15] FU Li-dong, LI Dan, LI Zhan-li. Following-degree Tree Algorithm to Detect Overlapping Communities in Complex Networks [J]. Computer Science, 2019, 46(12): 322-326.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .