Analysis of Chinese Airport Network’s Time-Varing and Multi-Layered Features
LUO Yunqian1,2,3, TANG Jinhui1,2, ZHAO Zhonglei1,2, ZHU Yongwen1,2, DONG Xiangjun1,2
1. The Army 95899 of PLA, Beijing 100085, China; 2. National Key Laboratory of Airspace Technology, Beijing 100085, China; 3. The Army 95865 of PLA, Beijing 100085, China
Abstract:Chinese air transport network’s time-varying and multi-layered featur are studieds by complex network theory. The results demonstrate that the three networks have similar features on degree distribution, short average path length, clustering coefficient, rich club coefficient, and assortativity coefficient, but the result of network similarity comparison demonstrates that the inner structures of the three networks are quite different. The transfer airline network will play important roles in optimization of the network structure.
[1] 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. Liu Hongkun,Zhou Tao. Empirical study of Chinese city airline network[J]. Acta Phys Sin, 2007, 56(1):106-112. [2] Massimiliano Z, Fabrizio L. Modelling the air transport with complex networks:a short review[J]. The European Physical Journal Special Topics, 2013, 215(1):5-21. [3] Tang J, Scellato S, Musolesi M, et al. Small-world behavior in time-varying graphs[J].Physical Review E,2010, 81(5):055101. [4] Mucha P J, Richardson T, Macon K, et al. Community structure in time-dependent, multiscale, and multiplex networks[J]. Science, 2010, 328(5980):876-878. [5] Zhao K, Stehle J, Bianconi G, et al. Social network dynamics of face-to-face interactions[J]. Physical Review E, 2011, 83(5):056109. [6] Kurant M, Thiran P. Layered complex networks[J]. Physical Review Letters, 2006,96(1):138701. [7] Gao J X, Buldyrev S V, Stanley H E, et al. Networks formed from interdependent networks[J]. Nature Physics,2012,8(1):40-48. [8] Gomez-Gardenes J, Reinares I, Arenas A, et al. Evolution of cooperation in multiplex networks[DB/OL]. (2012-08-31)[2014-03-20].http://www.nature.com/srep/2012/120831/srep00620/full/srep00620.html. [9] Li W, Cai X. Statistical analysis of airport network of China[J]. Physical Review E, 2004, 69(4):046106. [10] Han D D, Qian J H, Liu J G. Network topology and correlation features affiliated with European airline companies[J]. Physica A, 2009,388(1):71-81. [11] Gautreau A, Barrat A, Barthelemy M. Microdynamics in stationary complex networks[J].Proceedings of the National Academy of Sciences. 2009, 106(22):8847-8852. [12] Rocha L E C. Structural evolution of the Brazilian airport network[DB/OL]. (2009-04-09)[2014-03-20].http://arxiv.org/pdf/0804.308/v3.pdf. [13] Cai K Q, Zhang J, Du W B, et al. Analysis of the Chinese air route network as a complex network[J]. Chin Phys B, 2012, 21(2):028903. [14] 武文杰, 董正斌, 张文忠, 等. 中国城市空间关联网络结构的时空演变[J]. 地理学报, 2011, 66(4):435-445. Wu Wengjie, Dong Zhengbin, Zhang Wenzhong, et al. Spatio-temporal evolution of the China’s inter-urban organization network structure:based on aviation data from 1983 to 2006[J]. Acta Geographica Sinica, 2011, 66(4):435-445. [15] Alessio C, Jesus G G, Massimiliano Z, et al. Emergence of network features from multiplexity[DB/OL].(2013-03-04)[2014-03-20].http://arxiv.org/pdf/1212.2153v2.pdf. [16] Chen G R, Wang X F, Li X. Introduction to Complex Networks:Models, Structures and Dynamics[M]. Beijing:Higher Education Press,2012. [17] Panagiotis P, Ali D, Hector G-M. Web graph similarity for anomaly detection[J]. Journal of Internet Services and Applications,2010,1(1):19-30. [18] 曾小舟.基于复杂网络理论的中国航空网络结构实证研究与分析[D]. 南京:南京航空航天大学博士学位论文,2011. Zeng Xiaozhou. Empirical study of Chinese airline network structure based on complex network theory[D]. Nanjing:Nanjing University of Aeronautics and Astronautics,2011.