Google hyperlinks

This is a network of web pages connected by hyperlinks. The data was released in 2002 by Google as a part of the Google Programming Contest.


Internal nameweb-Google
NameGoogle hyperlinks
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Hyperlink network
Node meaningWebpage
Edge meaningHyperlink
Network formatUnipartite, directed
Edge typeUnweighted, no multiple edges
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsDoes not contain loops


Size n =875,713
Volume m =5,105,039
Wedge count s =727,417,224
Claw count z =667,827,082,809
Cross count x =649,372,878,638,139
Triangle count t =13,391,903
Square count q =539,575,204
4-Tour count T4 =7,234,914,630
Maximum degree dmax =6,353
Maximum outdegree d+max =456
Maximum indegree dmax =6,326
Average degree d =11.659 2
Fill p =6.656 96 × 10−6
Size of LCC N =855,802
Size of LSCC Ns =434,818
Relative size of LSCC Nrs =0.496 530
Diameter δ =24
50-Percentile effective diameter δ0.5 =5.742 34
90-Percentile effective diameter δ0.9 =7.948 20
Mean distance δm =6.373 75
Gini coefficient G =0.597 285
Relative edge distribution entropy Her =0.941 199
Power law exponent γ =1.617 21
Tail power law exponent γt =2.731 00
Degree assortativity ρ =−0.055 089 0
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.388 241
Clustering coefficient c =0.055 230 6
Spectral norm α =116.964
Operator 2-norm ν =105.911
Cyclic eigenvalue π =37.639 6
Algebraic connectivity a =0.002 704 91
Reciprocity y =0.306 751
Non-bipartivity bA =0.174 111
Normalized non-bipartivity bN =0.001 246 93
Spectral bipartite frustration bK =9.439 92 × 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

In/outdegree scatter plot

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.