New York City

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

Metadata

Code9N
Internal namedimacs9-NY
NameNew York City
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 =264,346
Volume m =730,100
Wedge count s =769,738
Claw count z =6,016,896
Cross count x =6,221,822
Triangle count t =6,529
Square count q =44,950
4-Tour count T4 =4,168,652
Maximum degree dmax =16
Maximum outdegree d+max =8
Maximum indegree dmax =8
Average degree d =5.523 82
Fill p =1.044 81 × 10−5
Size of LCC N =264,346
Size of LSCC Ns =264,346
Relative size of LSCC Nrs =1.000 00
Diameter δ =720
50-Percentile effective diameter δ0.5 =257.770
90-Percentile effective diameter δ0.9 =424.937
Mean distance δm =261.456
Gini coefficient G =0.189 062
Relative edge distribution entropy Her =0.994 341
Power law exponent γ =2.074 97
Tail power law exponent γt =8.991 00
Tail power law exponent with p γ3 =8.991 00
p-value p =0.000 00
Outdegree tail power law exponent with p γ3,o =8.991 00
Outdegree p-value po =0.000 00
Indegree tail power law exponent with p γ3,i =8.991 00
Indegree p-value pi =0.000 00
Degree assortativity ρ =+0.178 503
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+1.000 00
Clustering coefficient c =0.025 446 3
Spectral norm α =8.609 12
Operator 2-norm ν =4.304 56
Cyclic eigenvalue π =4.304 56
Algebraic connectivity a =6.328 77 × 10−6
Reciprocity y =1.000 00
Non-bipartivity bA =0.086 135 1
Normalized non-bipartivity bN =0.000 780 111
Spectral bipartite frustration bK =0.000 141 331

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

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 ]