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.


Internal nameroadNet-TX
Data sourcehttp://snap.stanford.edu/data/roadNet-TX.html
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 =1,379,917
Volume m =1,921,660
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
4-Tour count T4 =21,821,436
Maximum degree dmax =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
50-Percentile effective diameter δ0.5 =456.877
90-Percentile effective diameter δ0.9 =698.834
Median distance δM =457
Mean distance δm =451.397
Gini coefficient G =0.188 538
Balanced inequality ratio P =0.437 059
Relative edge distribution entropy Her =0.994 702
Power law exponent γ =2.073 71
Tail power law exponent γt =8.901 00
Tail power law exponent with p γ3 =8.901 00
p-value p =0.174 000
Degree assortativity ρ =+0.130 404
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.060 224 2
Spectral norm α =4.906 12
Algebraic connectivity a =7.378 96 × 10−7
Spectral separation 1[A] / λ2[A]| =1.021 38
Non-bipartivity bA =0.194 545
Normalized non-bipartivity bN =0.001 117 89
Algebraic non-bipartivity χ =0.002 196 90
Spectral bipartite frustration bK =0.000 197 445
Controllability C =121,393
Relative controllability Cr =0.087 971 2


Fruchterman–Reingold graph drawing

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

Degree assortativity

Zipf plot

Hop distribution

Delaunay graph drawing

Clustering coefficient distribution

Average neighbor degree 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 Lang, Anirban Dasgupta, and Michael W. Mahoney. Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Math., 6(1):29–123, 2009.