California and Nevada

This is the directed road network from the 9th DIMACS Implementation Challenge, for the area "California and Nevada".

Metadata

Code9C
Internal namedimacs9-CAL
NameCalifornia and Nevada
Data sourcehttp://www.diag.uniroma1.it/challenge9/download.shtml
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Category
Infrastructure network
Dataset timestamp 1991
Node meaningNode
Edge meaningRoad
Network formatUnipartite, directed
Edge typeUnweighted, no multiple edges
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsDoes not contain loops

Statistics

Size n =1,890,815
Volume m =4,630,444
Loop count l =0
Wedge count s =4,192,229
Claw count z =30,258,364
Cross count x =28,228,721
Triangle count t =34,672
Square count q =134,075
4-Tour count T4 =22,471,960
Maximum degree dmax =14
Maximum outdegree d+max =7
Maximum indegree dmax =7
Average degree d =4.897 83
Fill p =1.295 16 × 10−6
Size of LCC N =1,890,815
Size of LSCC Ns =1,890,815
Relative size of LSCC Nrs =1.000 00
Diameter δ =2,315
50-Percentile effective diameter δ0.5 =766.790
90-Percentile effective diameter δ0.9 =1,248.86
Median distance δM =767
Mean distance δm =778.470
Gini coefficient G =0.209 777
Balanced inequality ratio P =0.424 340
Outdegree balanced inequality ratio P+ =0.424 340
Indegree balanced inequality ratio P =0.424 340
Relative edge distribution entropy Her =0.994 497
Power law exponent γ =2.243 70
Degree assortativity ρ =+0.074 357 5
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+1.000 00
Clustering coefficient c =0.024 811 6
Directed clustering coefficient c± =0.024 811 6
Spectral norm α =8.605 43
Operator 2-norm ν =4.302 72
Cyclic eigenvalue π =4.302 72
Algebraic connectivity a =3.677 81 × 10−7
Reciprocity y =1.000 00
Non-bipartivity bA =0.087 808 9
Normalized non-bipartivity bN =0.000 181 755
Algebraic non-bipartivity χ =0.000 360 203
Spectral bipartite frustration bK =3.677 17 × 10−5

Plots

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

Hop distribution

In/outdegree scatter plot

Clustering coefficient 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 ]