Electronics and Electrical Engineering and Control

Air traffic network motif recognition and sub-graph structure resilience evaluation

  • WANG Xinglong ,
  • SHI Zongbei ,
  • CHEN Ziyan
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  • College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China

Received date: 2020-09-04

  Revised date: 2020-09-23

  Online published: 2020-10-30

Supported by

National Key Basic Research Program of China(2016YFB0502405); Fundamental Research Funds for the Central Universities (3122019D027)

Abstract

The structural characteristics of air traffic networks are important for the understanding of the macroscopic nature and resilience of the networks. The sub-graph structure of the air traffic network is studied from a local perspective to identify the characteristics of its phantom. By analyzing the change of sub-graph concentration under external disturbances, we propose the concept of sub-graph structural toughness to characterize the dynamic evolution law of the underlying network topology. The phantom characteristics of the low-order sub-graph structure are identified, and the changes in the structural toughness of the sub-graph under different disturbances are evaluated with the air traffic network in East China as an example. The empirical results show that the phantom characteristics of the sub-graph structure meet the practical connectivity requirements of the air traffic network; in the process of disturbances and network recovery, the relative concentration of the sub-graph is relatively stable, and the structural toughness of the sub-graph is more consistent with the changes in the macro structure of the network. It has certain research significance for revealing the preference of the connection between the nodes and the rationality of the route structure, the underlying mechanism behind the network disturbance and recovery process, and the relationship between the overall and local structures of the network.

Cite this article

WANG Xinglong , SHI Zongbei , CHEN Ziyan . Air traffic network motif recognition and sub-graph structure resilience evaluation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021 , 42(7) : 324715 -324715 . DOI: 10.7527/S1000-6893.2020.24715

References

[1] 武喜萍, 杨红雨, 韩松臣. 基于复杂网络的空中交通特征与延误传播分析[J]. 航空学报, 2017, 38(S1):721473. WU X P, YANG H Y, HAN S C. Analysis of properties and delay propagation of air traffic based on complex network[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(S1):721473(in Chinese).
[2] 李善梅. 空中交通拥挤的识别与预测方法研究[D]. 天津:天津大学, 2014:22-35. LI S M. Research on identification and prediction methods of air traffic congestion[D]. Tianjin:Tianjin University, 2014:22-35(in Chinese).
[3] PIEN K C, HAN K, SHANG W L, et al. Robustness analysis of the European air traffic network[J].Transportmetrica A:Transport Science, 2015, 11(9):772-792.
[4] XU Z W, HARRISS R. Exploring the structure of the US intercity passenger air transportation network:A weighted complex network approach[J].GeoJournal, 2008, 73(2):87-102.
[5] 王兴隆, 齐雁楠, 潘维煌. 基于功能脆弱性的空中交通相依网络流量分配[J]. 航空学报, 2020, 41(4):323479. WANG X L, QI Y N, PAN W H. Flow allocation of air traffic interdependent network based on functional vulnerability[J].Acta Aeronautica et Astronautica Sinica, 2020, 41(4):323479(in Chinese).
[6] ZHANG J, CAO X B, DU W B, et al.Evolution of Chinese airport network[J]. Physica A:Statistical Mechanics and Its Applications, 2010, 389(18):3922-3931.
[7] 陈娱, 王姣娥, 金凤君. 中国国内航空网络的可靠性评价[J]. 地理与地理信息科学, 2015, 31(3):59-64, 2. CHEN Y, WANG J E,JIN F J. Robustness and fragility of Chinese air transport network[J]. Geography and Geo-Information Science, 2015, 31(3):59-64, 2(in Chinese).
[8] DINH L T, PASMAN H, GAO X D, et al. Resilience engineering of industrial processes:Principles and contributing factors[J]. Journal of Loss Prevention in the Process Industries, 2012, 25(2):233-241.
[9] PRŽULJ N. Biological network comparison using graphlet degree distribution[J]. Bioinformatics, 2007, 23(2):e177-e183.
[10] MENCK P J, HEITZIG J, KURTHS J, et al. How dead ends undermine power grid stability[J]. Nature Communications, 2014, 5:3969.
[11] 李凡, 张杰勇. 指挥信息系统网络结构的韧性问题研究[J]. 电光与控制, 2020, 27(4):49-54. LI F, ZHANG J Y. On resilience of network structure of command information system[J]. Electronics Optics & Control, 2020, 27(4):49-54(in Chinese).
[12] TANG J Q, HEINIMANN H, HAN K, et al. Evaluating resilience in urban transportation systems for sustainability:A systems-based Bayesian network model[J]. Transportation Research Part C:Emerging Technologies, 2020, 121:102840.
[13] SHEN-ORR SS, MILO R, MANGAN S, et al. Network motifs in the transcriptional regulation network of Escherichia coli[J]. Nature Genetics, 2002, 31(1):64-68.
[14] SCHULTZ P, HEITZIG J, KURTHS J. Detours around basin stability in power networks[J]. New Journal of Physics, 2014, 16(12):125001.
[15] GOROCHOWSKI T E, GRIERSON C S, DI BERNARDO M. Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks[J]. Science Advances, 2018, 4(3):eaap9751.
[16] LESKOVEC J, HORVITZ E. Planetary-scale views on an instant-messaging network[DB/OL]. arxiv preprint:0803.0939,2008.
[17] 丁明, 韩平平. 加权拓扑模型下的小世界电网脆弱性评估[J]. 中国电机工程学报, 2008, 28(10):20-25. DING M, HAN PP. Vulnerability assessment to small-world power grid based on weighted topological model[J]. Proceedings of the CSEE, 2008, 28(10):20-25(in Chinese).
[18] CHASSIN D P, POSSE C. Evaluating North American electric grid reliability using the Barabási-Albert network model[J]. Physica A:Statistical Mechanics and Its Applications, 2005, 355(2-4):667-677.
[19] 徐立新. 基于复杂系统理论的电网故障时空分布特性及结构脆弱性研究[D]. 广州:华南理工大学, 2014:90-99. XU L X. Study on fault's temporal-spatial distribution characteristics and structural vulnerability in power grid based on complex system theory[D]. Guangzhou:South China University of Technology, 2014:90-99(in Chinese).
[20] 刘咏梅, 彭琳, 赵振军. 基于小世界网络的微博谣言传播演进研究[J]. 复杂系统与复杂性科学, 2014, 11(4):54-60. LIU Y M, PENG L, ZHAO Z J. The evolution of rumor spread onmicrblog based on small-world network[J]. Complex Systems and Complexity Science, 2014, 11(4):54-60(in Chinese).
[21] KURANT M, THIRAN P. Extraction and analysis of traffic and topologies of transportation networks[J]. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2006, 74(3pt 2):036114.
[22] WERNICKE S, RASCHE F. FANMOD:A tool for fast network motif detection[J]. Bioinformatics, 2006, 22(9):1152-1153.
[23] WERNICKE S. Efficient detection of network motifs[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2006, 3(4):347-359.
[24] OSEI-ASAMOAH A, LOWNES N E. Complex network method of evaluating resilience in surface transportation networks[J]. Transportation Research Record:Journal of the Transportation Research Board, 2014, 2467(1):120-128.
[25] WANG Y J, ZHAN J M, XU X H, et al. Measuring the resilience of an airport network[J]. Chinese Journal of Aeronautics, 2019, 32(12):2694-2705.
[26] 李兆隆, 金淳, 胡畔, 等. 基于弹复性的交通网络应急恢复阶段策略优化[J]. 系统工程理论与实践, 2019, 39(11):2828-2841. LI Z L, JIN C, HU P, et al.Resilience-based recovery strategy optimization in emergency recovery phase for transportation networks[J]. Systems Engineering-Theory & Practice, 2019, 39(11):2828-2841(in Chinese).
[27] 缪莉莉, 韩传峰, 刘亮, 等. 基于模体的科学家合作网络基元特征分析[J]. 科学学研究, 2012, 30(10):1468-1475. MIAO L L, HAN C F, LIU L, et al. Using motif to characterize building block of scientific collaboration networks[J]. Studies in Science of Science, 2012, 30(10):1468-1475(in Chinese).
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