Abstract

We analyze structure and dynamics of flight networks of 50 airlines active in the European airspace in 2017. Our analysis shows that the concentration of the degree of nodes of different flight networks of airlines is markedly heterogeneous among airlines reflecting heterogeneity of the airline business models. We obtain an unsupervised classification of airlines by performing a hierarchical clustering that uses a correlation coefficient computed between the average occurrence profiles of 4-motifs of airline networks as similarity measure. The hierarchical tree is highly informative with respect to properties of the different airlines (for example, the number of main hubs, airline participation to intercontinental flights, regional coverage, nature of commercial, cargo, leisure or rental airline). The 4-motif patterns are therefore distinctive of each airline and reflect information about the main determinants of different airlines. This information is different from what can be found looking at the overlap of directed links.

Highlights

  • Dynamics of European FlightThe air transportation system (ATS) is a socio-technical system analyzed as a complex network for many years [1,2]

  • We have analyzed the structure and dynamics of flight networks of 50 airlines performing most of the flights that occurred in the European airspace in 2017

  • Our analysis of directed flight networks shows that the degree concentration of the different networks is quite heterogeneous among the different airlines

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Summary

Introduction

The air transportation system (ATS) is a socio-technical system analyzed as a complex network for many years [1,2]. Flight networks have been investigated from different perspectives and at different scales [16,17,18], for example, by considering basic network metrics, topology of the degree distribution, resilience to attack or failures, community detection of large clusters and computation and analysis of network motifs. By investigating the number and temporal evolution of the 3- and 4-motifs, we are able to perform an unsupervised classification of the 50 airlines indicating that main differences among different airlines are due to their regional specialization (including the ability to perform intercontinental flights) and to their business model.

Data and Methods
Flight Data
Herfindal Index
Motifs Detection
Average Linkage Clustering Analysis
Daily Occurrence of 4-Motifs
Similarity of 4-Motif Profile
Airline Networks Overlap
Discussion and Conclusions
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