Florida

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

Metadata

Code9F
Internal namedimacs9-FLA
NameFlorida
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 =1,070,376
Volume m =2,687,902
Loop count l =0
Wedge count s =2,519,132
Claw count z =18,454,440
Cross count x =17,451,504
Triangle count t =22,327
Square count q =110,893
4-Tour count T4 =13,651,574
Maximum degree dmax =16
Maximum outdegree d+max =8
Maximum indegree dmax =8
Average degree d =5.022 35
Fill p =2.346 07 × 10−6
Size of LCC N =1,070,376
Size of LSCC Ns =1,070,376
Relative size of LSCC Nrs =1.000 00
Diameter δ =2,058
50-Percentile effective diameter δ0.5 =538.354
90-Percentile effective diameter δ0.9 =1,082.86
Median distance δM =539
Mean distance δm =597.595
Gini coefficient G =0.206 181
Balanced inequality ratio P =0.427 587
Outdegree balanced inequality ratio P+ =0.427 587
Indegree balanced inequality ratio P =0.427 587
Relative edge distribution entropy Her =0.994 300
Power law exponent γ =2.207 68
Tail power law exponent γt =6.431 00
Degree assortativity ρ =+0.080 160 6
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+1.000 00
Clustering coefficient c =0.026 588 9
Directed clustering coefficient c± =0.026 588 9
Spectral norm α =8.250 97
Operator 2-norm ν =4.125 49
Cyclic eigenvalue π =4.125 49
Algebraic connectivity a =3.856 63 × 10−7
Reciprocity y =1.000 00
Non-bipartivity bA =0.044 432 2
Normalized non-bipartivity bN =0.000 176 070
Algebraic non-bipartivity χ =0.000 350 119
Spectral bipartite frustration bK =3.485 61 × 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 ]