eu_airlines — European airline network

Description

A multiplex network of the routes operated by 37 airlines between European airports. Nodes are airports, and an undirected edge in layer $\ell$ connects two airports if airline $\ell$ operated a flight between them. The data were compiled by A. Cardillo and collaborators from the complete list of commercial flights operating under Instrument Flight Rules (IFR) between European airports on a single day (1 June 2011), obtained from EUROCONTROL (via the ComplexWorld Network / SESAR Work Package E). Only the 37 airlines whose number of destinations exceeds the average (32) were kept; they include major carriers (e.g. Lufthansa, Air France), low-cost carriers (e.g. Ryanair, Easyjet), and a few cargo and business-aviation operators (e.g. TNT Airways, Netjets). The edges are unweighted, and the same pair of airports may be connected in several layers.

The network has the following properties:

name and nodeLabel: the ICAO code of the airport (two airports have placeholder codes XXXX and YYYY).
nodeLong and nodeLat: longitude and latitude of the airport, in degrees (0 for the two airports with placeholder codes).
layer (edges): the airline (layer) operating the route, numbered 1 to 37; the graph property layer_key maps these numbers to airline names.
weight (edges): always 1.

Tags

Transportation Airport Multilayer Unweighted

Citation

Alessio Cardillo, Jesús Gómez-Gardenes, Massimiliano Zanin, Miguel Romance, David Papo, Francisco del Pozo and Stefano Boccaletti. "Emergence of network features from multiplexity." Scientific Reports 3, Article number: 1344 doi:10.1038/srep01344 10.1038/srep01344 Sci-Hub

Upstream URL OK

https://manliodedomenico.com/data.php

Upstream license

GNU Free Documentation License (GFDL) 1.3, https://www.gnu.org/licenses/fdl-1.3.html

Networks

Tip: hover over a column header for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
eu_airlines	450	3,588	15.95	27.12	40.73	11.86	-0.05	0.38	7	0.93	Undirected	Unipartite	name nodeLabel nodeLong nodeLat	weight layer	20 KiB	31 KiB	30 KiB	23 KiB

Ridiculograms^*

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.