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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["us_air_traffic"]

us_air_traffic — U.S. air traffic


Yearly snapshots of flights among all commercial airports in the United States from 1990 to today. Metadata include passengers, distance, carrier, airport located city, state, and month of the flight.1

  1. Description obtained from the ICON project. 

Transportation Airport Unweighted Metadata Temporal
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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
us_air_traffic 2,278 6,390,340 2805.24 23318.93 225.51 41.03 0.03 0.92 6 1.00 Directed Unipartite airport_code airport_id airport_seq_id city_market_id origin city_name state_abr state_fips state_nm wac passengers freight mail distance unique_carrier airline_id unique_carrier_name unique_carrier_entity region carrier carrier_name carrier_group carrier_group_new year quarter month distance_group class 65.4 MiB 84.7 MiB 55.4 MiB 69.9 MiB
None drawing