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You may also take a look at the source code.
The network in this dataset can be loaded directly from graph-tool with:
import graph_tool.all as gt
g = gt.collection.ns["openflights"]

openflights — Openflights airport network

Description

A network of regularly occurring flights among airports worldwide, extracted from the openflights.org dataset. Nodes represent airports, and direction of edge (i,j) indicates a regularly occurring commercial flight by a particular airline from airport i to airport j. Multiple edges may exist between a pair of airports if multiple airlines offer that flight, or if one airline offers multiple such flights each day.1


  1. Description obtained from the ICON project. ↩

Tags
Transportation Airport Weighted Multigraph
Citation
Upstream URL OK
https://openflights.org/data.html
Networks
Tip: hover your mouse over a table header to obtain a legend.
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv
openflights 3,214 66,771 20.78 109.43 68.15 51.53 0.00 0.34 12 0.99 Directed Unipartite id name city country IATA/FAA ICAO latitude longitude altitude timezone DST distance airline airline_code codeshare equipment stops 607 KiB 977 KiB 796 KiB 905 KiB
Ridiculograms*
None drawing
* 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.