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.


Internal nameroadNet-CA
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Infrastructure network
Node meaningIntersection/endpoint
Edge meaningConnection
Network formatUnipartite, undirected
Edge typeUnweighted, no multiple edges
LoopsDoes not contain loops


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
4-Tour count T4 =31,612,286
Maximum degree dmax =12
Average degree d =2.815 59
Fill p =1.432 72 × 10−6
Size of LCC N =1,957,027
Diameter δ =865
50-Percentile effective diameter δ0.5 =303.852
90-Percentile effective diameter δ0.9 =511.075
Mean distance δm =315.889
Gini coefficient G =0.185 512
Balanced inequality ratio P =0.438 309
Relative edge distribution entropy Her =0.995 082
Power law exponent γ =2.055 71
Tail power law exponent γt =8.991 00
Degree assortativity ρ =+0.126 042
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.060 387 4
Spectral norm α =4.638 36
Algebraic connectivity a =5.646 78 × 10−7
Non-bipartivity bA =0.152 274
Normalized non-bipartivity bN =0.000 418 791
Spectral bipartite frustration bK =7.347 08 × 10−5


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

Clustering coefficient distribution


Matrix decompositions plots



[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.