为了更深入研究中国航线网络的拓扑特性中的细节问题,运用复杂网络理论,将每个航空公司定义为网络中的一个层并建立多层网络模型,仿真逐层合并过程中网络特征参数的演变,探讨中国航空多层网络(CAMN)的拓扑新特性。结果表明,CAMN总度值呈现幂律分布,总度值高的五大机场其度值在各航空公司间分布均匀;中国航空多层网络在聚合过程中都呈现无标度网络特性,而“小世界网络”特性仅在较多数量层的网络聚合时明显,且成规模的航空公司合作使网络的运输效率更高;中国航空聚合网络的“小世界”现象主要是由大中型航空公司对应层引起,大中型航空公司网络的运输效率比廉价航空公司网络高,但同质性更低。
To further study the details of the topological characteristics of Chinese airline network, this work explored the new topological features of China Aviation Multi-layer Network (CAMN) based on complex network theory. We defined each airline as a layer in network and established a multi-layer network by simulating the evolution of network characteristic parameters in the layer-by-layer process. The results indicate that overlapping degree value of CAMN obeys a power-law distribution and five airports with highest overlapping value are uniformly distributed among airlines. Meanwhile, CAMN presents the characteristics of scale-free networks in aggregation, but characteristics of the small world network can only be obviously observed in aggregation of multi-layer networks. Further, the cooperation of large-scale airlines makes network more efficient. The phenomenon of small world in Chinese aviation aggregation network is mainly caused by the corresponding layer of large and medium-sized airlines. The transportation efficiency of large and medium-sized airline networks is higher than that of low-cost airlines, but the homogeneity is poorer.
[1]R. Guimerá, Amaral L A N. Modeling the world-wide airport network[J]. European Physical Journal B, 2004, 38(2):381385.
[2]Gautreau A, Barrat A, Barthelemy M. Microdynamics in stationary complex networks[J]. Proceedings of the National Academy of Sciences, 2009, 106(22):88478852.
[3]Cai K Q, Zhang J, Du W B, et al. Analysis of the Chinese air route network as a complex network[J]. Chinese Physics B, 2012, 21(2):028903.
[4]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):106112.
Liu Hongkun, Zhou Tao. Empirical study of Chinese city airline network[J]. Acta Physica Sinica, 2007, 56(1):106112.
[5]Bagler G. Analysis of the airport network of India as a complex weighted network[J]. Physica A Statistical Mechanics & Its Applications, 2008, 387(12):29722980.
[6]Boccaletti S, Bianconi G, Criado R, et al. The structure and dynamics of multilayer networks[J]. Physics Reports, 2014,544(1):1122.
[7]Gómez-Gardees J, Reinares I, Arenas A, et al. Evolution of cooperation in multiplex networks[J]. Scientific Reports, 2012, 6(620):16.
[8]Du W B, Zhou X L, Lordan O, et al. Analysis of the Chinese airline network as multi-layer networks[J]. Transportation Research Part E: Logistics and Transportation Review, 2016, 89:108116.
[9]Lordan O, Sallan J M. Analyzing the multilevel structure of the European airport network[J]. Chinese Journal of Aeronautics, 2017, 30(2):554560.
[10] Cardillo A, Gómez-Gardenes J, Zanin M, et al. Emergence of network features from multiplexity[J]. Scientific Reports, 2013, 3:01344.
[11] Klophaus R, Lordan O. Codesharing network vulnerability of global airline alliances[J]. Transportation Research Part A: Policy and Practice, 2018, 111:110.
[12] Dai L, Derudder B, Liu X. The evolving structure of the Southeast Asian air transport network through the lens of complex networks, 1979–2012[J]. Journal of Transport Geography, 2018, 68:6777.
[13] 孙圣波, 朱保平, 杨晓光. 基于三角模体的社团发现算法[J]. 南京理工大学学报(自然科学版), 2017, 41(1):3540.
Sun Shengbo, Zhu Baoping, Yang Xiaoguang. Community discovery algorithm based on triangular motifs[J]. Journal of Nanjing University of Science and Technology, 2017, 41(1):3540.
[14] 李明高, 杜鹏, 朱宇婷. 城市轨道交通换乘节点与网络运行效率关系研究[J]. 交通运输系统工程与信息, 2015, 15(2):4853.
LI Minggao, DU Peng, ZHU Yuting. Effect of urban rail transit transfer nodes on network performance[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(2):4853.
[15] Stauffer D. Introduction to Percolation Theory[M]. London:Taylor & Francis, 1985.
[16] Guimerà, R, Díaz-Guilera, A, Vega-Redondo F, et al. Optimal network topologies for local search with congestion[J]. Physical Review Letters, 2002, 89(24):248701.
[17] Motter A E, Lai Y C. Dissipative chaotic scattering[J]. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2002, 65(1/2):015205.
[18] Deng Y. Generalized evidence theory[J]. Applied Intelligence, 2015, 43(3):530543.
[19] Jiang W, Yang Y, Luo Y,et al.Determining basic probability assignment based on the improved similarity measures of generalized fuzzy numbers[J]. International Journal of Computers Communications & Control,2015(3):333347.
[20] Battiston F, Nicosia V, Latora V. Structural measures for multiplex networks[J]. Physical Review E, 2014, 89(3):032804.
[21] 中国民用航空局.2017年民航行业发展统计公报[DB/OL]. [20190210]. http://www.caac.gov.cn/XXGK/XXGK/TJSJ.
Civil Aviation Administration of China. 2017 civil aviation industry development statistics bulletin[DB/OL]. [20190210]. http://www.caac.gov.cn/XXGK/XXGK/TJSJ.
[22] Wu J Y, Yang Y, Xu K J.Comparative analysis of Chinese airway network based on complex network[C]. International Conference on Advanced Cloud and Big Data (CBD).2018.
[23] Boccaletti S, Bianconi G, Criado R, et al. The structure and dynamics of multilayer networks[J]. Physics Reports, 2014, 544(1):1122.
[24] Min B, Yi S D, Lee K M, et al. Network robustness of multiplex networks with interlayer degree correlations[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2014, 89(4):042811.
[25] Zanin M, Lillo F. Modelling the air transport with complex networks: a short review[J]. The European Physical Journal Special Topics, 2013, 215(1):521.
[26] Guimerá, Amaral L A N. Modeling the world-wide airport network[J]. European Physical Journal B, 2004, 38(2):381385.