OpenFlights (Patokallio)

These are flights collected by the project. Each node is an airport, and a directed edge represents one flight by one airline. A flight in this dataset is defined as a normally regularly occuring flight identified by its flight number (e.g., AF331), not individual flights (e.g., AF331 on June 14 2015). In the network, multiple edges may connect the same nodes, because multiple airlines may connect the same airports, and a single airline may have multiple flights connecting the same airports (usually, at different time of days). Thus, the multiplicity of an edge gives a rough estimate of the traffic between two airports. The dataset in the version available contains at least one loop: A flight by Trigana Air Service (IATA code IL) from and to Iskandar Airport (IATA code PKN) in Indonesia. We do not know whether such entries are errors, or whether they represent legitimate flights, used e.g. for sightseeing.


Internal nameopenflights
NameOpenFlights (Patokallio)
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Infrastructure network
Node meaningAirport
Edge meaningFlight
Network formatUnipartite, directed
Edge typeUnweighted, multiple edges
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsContains loops


Size n =3,425
Volume m =67,663
Unique edge count m̿ =37,595
Loop count l =1
Wedge count s =1,221,697
Claw count z =383,135,844
Cross count x =29,224,814,720
Triangle count t =101,117
Square count q =4,243,727
4-Tour count T4 =38,875,116
Maximum degree dmax =1,826
Maximum outdegree d+max =915
Maximum indegree dmax =911
Average degree d =39.511 2
Fill p =0.003 204 86
Average edge multiplicity m̃ =1.799 79
Size of LCC N =3,397
Size of LSCC Ns =3,354
Relative size of LSCC Nrs =0.979 270
Diameter δ =13
50-Percentile effective diameter δ0.5 =3.539 75
90-Percentile effective diameter δ0.9 =5.395 08
Median distance δM =4
Mean distance δm =4.192 98
Gini coefficient G =0.781 382
Relative edge distribution entropy Her =0.863 738
Power law exponent γ =1.715 88
Tail power law exponent γt =1.741 00
Tail power law exponent with p γ3 =1.741 00
p-value p =0.000 00
Outdegree tail power law exponent with p γ3,o =1.721 00
Outdegree p-value po =0.000 00
Indegree tail power law exponent with p γ3,i =1.721 00
Indegree p-value pi =0.000 00
Degree assortativity ρ =−0.006 455 52
Degree assortativity p-value pρ =0.205 214
In/outdegree correlation ρ± =+0.995 878
Clustering coefficient c =0.248 303
Directed clustering coefficient c± =0.249 277
Spectral norm α =353.415
Operator 2-norm ν =176.748
Cyclic eigenvalue π =176.668
Algebraic connectivity a =0.047 360 0
Reciprocity y =0.975 582
Non-bipartivity bA =0.656 663
Normalized non-bipartivity bN =0.032 163 5
Spectral bipartite frustration bK =0.001 222 29


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


Matrix decompositions plots



[1] Jérôme Kunegis. KONECT – The Koblenz Network Collection. In Proc. Int. Conf. on World Wide Web Companion, pages 1343–1350, 2013. [ http ]