California
This is the road network of the State of California 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 =  1,965,206

Volume  m =  2,766,607

Loop count  l =  0

Wedge count  s =  5,995,090

Claw count  z =  2,952,147

Cross count  x =  550,344

Triangle count  t =  120,676

Square count  q =  262,339

4Tour count  T_{4} =  31,612,286

Maximum degree  d_{max} =  12

Average degree  d =  2.815 59

Fill  p =  1.432 72 × 10^{−6}

Size of LCC  N =  1,957,027

Diameter  δ =  865

50Percentile effective diameter  δ_{0.5} =  303.852

90Percentile effective diameter  δ_{0.9} =  511.075

Median distance  δ_{M} =  304

Mean distance  δ_{m} =  315.889

Gini coefficient  G =  0.185 512

Balanced inequality ratio  P =  0.438 309

Relative edge distribution entropy  H_{er} =  0.995 082

Power law exponent  γ =  2.055 71

Tail power law exponent  γ_{t} =  8.991 00

Degree assortativity  ρ =  +0.126 042

Degree assortativity pvalue  p_{ρ} =  0.000 00

Clustering coefficient  c =  0.060 387 4

Spectral norm  α =  4.638 36

Algebraic connectivity  a =  5.646 78 × 10^{−7}

Nonbipartivity  b_{A} =  0.152 274

Normalized nonbipartivity  b_{N} =  0.000 418 791

Algebraic nonbipartivity  χ =  0.000 829 045

Spectral bipartite frustration  b_{K} =  7.347 08 × 10^{−5}

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 J. Lang, Anirban Dasgupta, and Michael W. Mahoney.
Statistical properties of community structure in large social and
information networks.
In Proc. Int. World Wide Web Conf., pages 695–704, 2008.
