This is the road network of Pennsylvania 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-PA
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 =1,088,092
Volume m =1,541,898
Loop count l =0
Wedge count s =3,390,667
Claw count z =1,707,904
Cross count x =328,820
Triangle count t =67,150
Square count q =157,802
4-Tour count T4 =17,908,880
Maximum degree dmax =9
Average degree d =2.834 13
Fill p =2.604 68 × 10−6
Size of LCC N =1,087,562
Diameter δ =794
50-Percentile effective diameter δ0.5 =296.231
90-Percentile effective diameter δ0.9 =528.609
Median distance δM =297
Mean distance δm =312.578
Gini coefficient G =0.188 029
Balanced inequality ratio P =0.435 923
Relative edge distribution entropy Her =0.994 680
Power law exponent γ =2.052 82
Tail power law exponent γt =8.991 00
Tail power law exponent with p γ3 =8.991 00
p-value p =0.000 00
Degree assortativity ρ =+0.122 749
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.059 413 1
Spectral norm α =4.419 54
Algebraic connectivity a =1.708 84 × 10−6
Non-bipartivity bA =0.111 397
Normalized non-bipartivity bN =0.001 041 12
Spectral bipartite frustration bK =0.000 183 671


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 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.