Texas
This is the road network of Texas in the United States of America. The nodes of
the network are the intersections between roads and road endpoints, and the
edges are road segments between intersections and road endpoints. The network
is undirected.
Metadata
Statistics
Size  n =  1,379,917

Volume  m =  1,921,660

Loop count  l =  0

Wedge count  s =  4,128,025

Claw count  z =  2,004,918

Cross count  x =  352,846

Triangle count  t =  82,869

Square count  q =  183,252

4Tour count  T_{4} =  21,821,436

Maximum degree  d_{max} =  12

Average degree  d =  2.785 18

Fill  p =  2.018 37 × 10^{−6}

Size of LCC  N =  1,351,137

Diameter  δ =  1,064

50Percentile effective diameter  δ_{0.5} =  456.877

90Percentile effective diameter  δ_{0.9} =  698.834

Mean distance  δ_{m} =  451.397

Gini coefficient  G =  0.188 538

Balanced inequality ratio  P =  0.437 059

Relative edge distribution entropy  H_{er} =  0.994 702

Power law exponent  γ =  2.073 71

Tail power law exponent  γ_{t} =  8.901 00

Tail power law exponent with p  γ_{3} =  8.901 00

pvalue  p =  0.174 000

Degree assortativity  ρ =  +0.130 404

Degree assortativity pvalue  p_{ρ} =  0.000 00

Clustering coefficient  c =  0.060 224 2

Spectral norm  α =  4.906 12

Algebraic connectivity  a =  7.378 96 × 10^{−7}

Nonbipartivity  b_{A} =  0.194 545

Normalized nonbipartivity  b_{N} =  0.001 117 89

Spectral bipartite frustration  b_{K} =  0.000 197 445

Plots
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 ]

[2]

Jure Leskovec, Kevin Lang, Anirban Dasgupta, and Michael W. Mahoney.
Community structure in large networks: Natural cluster sizes and the
absence of large welldefined clusters.
Internet Math., 6(1):29–123, 2009.
