Reliability Analysis for Aviation Airline Network Based on Complex Network

In order to improve the reliability of aviation airline network, this paper presents an empirical analysis on the airline network structure of an aviation company in China from the perspective of complex network, and the calculation result of the statistical features and degree distribution of the network, proves that the network is a small-world network and a scale-free network. Four indicators, i.e. degree, closeness, vertex betweenness and flow betweenness, are utilized for aviation network centralization so as to distinguish the most appropriate method. The influence of nodes in local network is to be measured through the indicators. The results show that vertex betweenness can achieve the best aviation network centralization effect. Specifically, the centrality degree reaches 95.87%. On this basis, the network reliability is analyzed to discover that when two nodes with maximum degree or maximum betweenness are removed, the network performance is reduced by a half. Eventually, countermeasures are proposed for further improvement according to the results. In other words, complex network method is feasible used to analyze the topological structure and statistical features of aviation network. Based on this, a study is conduced to the network reliability and suggestions are proposed for optimizing the aviation network.

[1]  Beom Jun Kim,et al.  Attack vulnerability of complex networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Mark E. J. Newman.  A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[3]  W. Li,et al.  Statistical analysis of airport network of China. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Yaru Dang,et al.  Comparative Analysis on Weighted Network Structure of Air Passenger Flow of China and US , 2011 .

[5]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Roger Guimerà,et al.  Modeling the world-wide airport network , 2004 .

[7]  Jun Zhang,et al.  Analysis of the Chinese air route network as a complex network , 2012 .

[8]  Vito Latora,et al.  The network analysis of urban streets: A dual approach , 2006 .

[9]  Dang Ya-ru,et al.  Air Passenger Flow Structure Analysis with Network View , 2010 .

[10]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[11]  R. Guimerà,et al.  The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Guido Caldarelli,et al.  Algorithms for representing network centrality , groups and density and clustered graph representation , 2022 .

[13]  Thomas W. Valente,et al.  The stability of centrality measures when networks are sampled , 2003, Soc. Networks.

[14]  Noah E. Friedkin,et al.  Theoretical Foundations for Centrality Measures , 1991, American Journal of Sociology.

[15]  Zhu Jin-fu,et al.  Relative interval robust optimization of airline network designing , 2012 .

[16]  Alessandro Vespignani,et al.  The effects of spatial constraints on the evolution of weighted complex networks , 2005, physics/0504029.