Autonomous systems (DIMACS10)

This is a snapshot of the structure of the Internet at the level of autonomous systems, reconstructed from BGP tables posted by the University of Oregon Route Views Project.


Internal namedimacs10-as-22july06
NameAutonomous systems (DIMACS10)
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Computer network
Dataset timestamp 2006
Node meaningAutonomous system
Edge meaningConnection
Network formatUnipartite, undirected
Edge typeUnweighted, no multiple edges
LoopsDoes not contain loops
Snapshot Is a snapshot and likely to not contain all data
Orientation Is not directed, but the underlying data is


Size n =22,963
Volume m =48,436
Loop count l =0
Wedge count s =12,615,661
Claw count z =6,012,695,865
Cross count x =2,783,793,490,302
Triangle count t =46,873
Square count q =3,089,604
4-Tour count T4 =75,276,348
Maximum degree dmax =2,390
Average degree d =4.218 61
Fill p =0.000 183 721
Size of LCC N =22,963
Diameter δ =11
50-Percentile effective diameter δ0.5 =3.215 61
90-Percentile effective diameter δ0.9 =4.468 21
Median distance δM =4
Mean distance δm =3.738 56
Gini coefficient G =0.631 878
Balanced inequality ratio P =0.265 133
Relative edge distribution entropy Her =0.836 172
Power law exponent γ =2.435 17
Tail power law exponent γt =2.091 00
Tail power law exponent with p γ3 =2.091 00
p-value p =0.647 000
Degree assortativity ρ =−0.198 385
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.011 146 4
Spectral norm α =71.613 0
Algebraic connectivity a =0.050 699 4
Spectral separation 1[A] / λ2[A]| =1.310 57
Non-bipartivity bA =0.236 971
Normalized non-bipartivity bN =0.036 705 4
Algebraic non-bipartivity χ =0.071 860 9
Spectral bipartite frustration bK =0.004 258 56
Controllability C =16,374
Relative controllability Cr =0.713 060


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

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 ]