US airports

This is the directed network of flights between US airports in 2010. Each edge represents a connection from one airport to another, and the weight of an edge shows the number of flights on that connection in the given direction, in 2010.

Metadata

CodeAF
Internal nameopsahl-usairport
NameUS airports
Data sourcehttp://toreopsahl.com/datasets/#usairports
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Category
Infrastructure network
Node meaningAirport
Edge meaningFlight
Network formatUnipartite, directed
Edge typePositive weights, no multiple edges
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsDoes not contain loops

Statistics

Size n =1,574
Volume m =28,236
Loop count l =0
Wedge count s =1,914,691
Claw count z =612,363,375
Cross count x =60,144,933,383
Triangle count t =245,172
Square count q =19,604,634
4-Tour count T4 =164,530,266
Maximum degree dmax =596
Maximum outdegree d+max =302
Maximum indegree dmax =294
Average degree d =35.878 0
Fill p =0.011 404 3
Size of LCC N =1,572
Size of LSCC Ns =1,402
Relative size of LSCC Nrs =0.890 724
Diameter δ =8
50-Percentile effective diameter δ0.5 =2.592 05
90-Percentile effective diameter δ0.9 =3.854 67
Median distance δM =3
Mean distance δm =3.136 85
Gini coefficient G =0.753 400
Relative edge distribution entropy Her =0.841 657
Power law exponent γ =1.546 88
Tail power law exponent γt =1.851 00
Tail power law exponent with p γ3 =1.851 00
p-value p =0.000 00
Outdegree tail power law exponent with p γ3,o =1.851 00
Outdegree p-value po =0.000 00
Indegree tail power law exponent with p γ3,i =1.861 00
Indegree p-value pi =0.000 00
Degree assortativity ρ =−0.113 295
Degree assortativity p-value pρ =9.930 38 × 10−99
In/outdegree correlation ρ± =+0.966 816
Clustering coefficient c =0.384 143
Directed clustering coefficient c± =0.363 179
Operator 2-norm ν =1.191 89 × 107
Cyclic eigenvalue π =1.191 87 × 107
Algebraic connectivity a =0.547 408
Spectral separation 1[A] / λ2[A]| =2.701 67
Reciprocity y =0.780 635
Non-bipartivity bA =0.723 066
Normalized non-bipartivity bN =0.160 504
Algebraic non-bipartivity χ =0.246 652
Spectral bipartite frustration bK =0.002 815 56
Controllability C =597
Relative controllability Cr =0.379 288

Plots

Fruchterman–Reingold graph drawing

Degree distribution

Cumulative degree distribution

Lorenz curve

Spectral distribution of the adjacency matrix

Spectral distribution of the normalized adjacency matrix

Spectral distribution of the Laplacian

Spectral graph drawing based on the adjacency matrix

Spectral graph drawing based on the Laplacian

Spectral graph drawing based on the normalized adjacency matrix

Degree assortativity

Zipf plot

Hop distribution

Double Laplacian graph drawing

Delaunay graph drawing

In/outdegree scatter plot

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree distribution

SynGraphy

Matrix decompositions plots

Downloads

References

[1] Jérôme Kunegis. KONECT – The Koblenz Network Collection. In Proc. Int. Conf. on World Wide Web Companion, pages 1343–1350, 2013. [ http ]
[2] Tore Opsahl. Why anchorage is not (that) important: Binary ties and sample selection, 2011. [ http ]