Wikipedia edits (pcd)

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


Internal nameedit-pcdwiki
NameWikipedia edits (pcd)
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 =8,580
Left size n1 =787
Right size n2 =7,793
Volume m =50,982
Unique edge count m̿ =25,944
Wedge count s =18,665,545
Claw count z =17,558,238,574
Cross count x =15,851,158,124,701
Square count q =14,356,382
4-Tour count T4 =189,604,408
Maximum degree dmax =8,477
Maximum left degree d1max =8,477
Maximum right degree d2max =189
Average degree d =11.883 9
Average left degree d1 =64.780 2
Average right degree d2 =6.542 02
Fill p =0.004 230 17
Average edge multiplicity m̃ =1.965 08
Size of LCC N =8,080
Diameter δ =13
50-Percentile effective diameter δ0.5 =3.071 01
90-Percentile effective diameter δ0.9 =3.931 14
Median distance δM =4
Mean distance δm =3.231 89
Gini coefficient G =0.819 555
Balanced inequality ratio P =0.173 846
Left balanced inequality ratio P1 =0.061 511 9
Right balanced inequality ratio P2 =0.228 551
Relative edge distribution entropy Her =0.748 109
Power law exponent γ =2.384 82
Tail power law exponent γt =2.741 00
Tail power law exponent with p γ3 =2.741 00
p-value p =0.000 00
Left tail power law exponent with p γ3,1 =1.551 00
Left p-value p1 =0.010 000 0
Right tail power law exponent with p γ3,2 =5.001 00
Right p-value p2 =0.191 000
Degree assortativity ρ =−0.326 220
Degree assortativity p-value pρ =0.000 00
Spectral norm α =323.273
Algebraic connectivity a =0.027 794 5
Spectral separation 1[A] / λ2[A]| =1.671 36
Controllability C =7,147
Relative controllability Cr =0.843 702


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.