Wikipedia edits (gn)

This is the bipartite edit network of the Guarani Wikipedia. It contains users and pages from the Guarani Wikipedia, connected by edit events. Each edge represents an edit. The dataset includes the timestamp of each edit.


Internal nameedit-gnwiki
NameWikipedia edits (gn)
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Authorship network
Dataset timestamp 2017-10-20
Node meaningUser, article
Edge meaningEdit
Network formatBipartite, undirected
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps


Size n =8,568
Left size n1 =1,063
Right size n2 =7,505
Volume m =91,541
Unique edge count m̿ =42,664
Wedge count s =25,335,887
Claw count z =18,323,921,216
Cross count x =12,610,534,328,234
Square count q =63,075,790
4-Tour count T4 =606,080,032
Maximum degree dmax =7,068
Maximum left degree d1max =7,068
Maximum right degree d2max =264
Average degree d =21.368 1
Average left degree d1 =86.115 7
Average right degree d2 =12.197 3
Fill p =0.005 347 83
Average edge multiplicity m̃ =2.145 63
Size of LCC N =7,914
Diameter δ =12
50-Percentile effective diameter δ0.5 =3.193 15
90-Percentile effective diameter δ0.9 =4.891 80
Median distance δM =4
Mean distance δm =3.538 68
Gini coefficient G =0.851 937
Balanced inequality ratio P =0.148 185
Left balanced inequality ratio P1 =0.069 225 8
Right balanced inequality ratio P2 =0.186 157
Relative edge distribution entropy Her =0.765 559
Power law exponent γ =2.051 69
Tail power law exponent γt =1.701 00
Tail power law exponent with p γ3 =1.701 00
p-value p =0.000 00
Left tail power law exponent with p γ3,1 =1.681 00
Left p-value p1 =0.000 00
Right tail power law exponent with p γ3,2 =5.851 00
Right p-value p2 =0.035 000 0
Degree assortativity ρ =−0.356 766
Degree assortativity p-value pρ =0.000 00
Spectral norm α =487.089
Algebraic connectivity a =0.049 143 2
Spectral separation 1[A] / λ2[A]| =2.314 11
Controllability C =6,515
Relative controllability Cr =0.766 200


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

Double Laplacian graph drawing

Delaunay graph drawing

Edge weight/multiplicity distribution

Temporal distribution

Temporal hop distribution

Diameter/density evolution

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] Wikimedia Foundation. Wikimedia downloads., January 2010.