Great Lakes

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

Metadata

Code9L
Internal namedimacs9-LKS
NameGreat Lakes
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 =2,758,119
Volume m =6,794,808
Loop count l =0
Wedge count s =6,157,714
Claw count z =44,551,232
Cross count x =42,027,094
Triangle count t =47,051
Square count q =224,038
4-Tour count T4 =33,217,968
Maximum degree dmax =16
Maximum outdegree d+max =8
Maximum indegree dmax =8
Average degree d =4.927 13
Fill p =8.932 05 × 10−7
Size of LCC N =2,758,119
Size of LSCC Ns =2,758,119
Relative size of LSCC Nrs =1.000 00
Diameter δ =4,127
50-Percentile effective diameter δ0.5 =1,104.33
90-Percentile effective diameter δ0.9 =2,377.70
Mean distance δm =1,239.73
Gini coefficient G =0.205 838
Balanced inequality ratio P =0.423 419
Outdegree balanced inequality ratio P+ =0.423 419
Indegree balanced inequality ratio P =0.423 419
Relative edge distribution entropy Her =0.994 915
Power law exponent γ =2.225 85
Tail power law exponent γt =6.141 00
Degree assortativity ρ =+0.096 074 9
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+1.000 00
Clustering coefficient c =0.022 923 0
Spectral norm α =9.039 66
Operator 2-norm ν =4.519 83
Cyclic eigenvalue π =4.519 83
Algebraic connectivity a =1.167 59 × 10−7
Reciprocity y =1.000 00
Non-bipartivity bA =0.126 633
Normalized non-bipartivity bN =7.028 07 × 10−5
Spectral bipartite frustration bK =1.431 10 × 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

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 ]