Wikipedia talk (br)

This is the communication network of the Breton Wikipedia. Nodes represent users, and an edge from user A to user B denotes that user A wrote a message on the talk page of user B at a certain timestamp.


Internal namewiki_talk_br
NameWikipedia talk (br)
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Communication network
Dataset timestamp 2017-10-27
Node meaningUser
Edge meaningMessage
Network formatUnipartite, directed
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsContains loops


Size n =1,181
Volume m =13,754
Unique edge count m̿ =3,016
Loop count l =3,363
Wedge count s =196,495
Claw count z =18,620,328
Cross count x =1,255,334,749
Triangle count t =2,688
Square count q =99,938
4-Tour count T4 =1,590,144
Maximum degree dmax =3,297
Maximum outdegree d+max =2,504
Maximum indegree dmax =1,418
Average degree d =23.292 1
Fill p =0.002 162 38
Average edge multiplicity m̃ =4.560 34
Size of LCC N =1,021
Size of LSCC Ns =146
Relative size of LSCC Nrs =0.123 624
Diameter δ =8
50-Percentile effective diameter δ0.5 =2.499 82
90-Percentile effective diameter δ0.9 =3.716 44
Median distance δM =3
Mean distance δm =3.029 72
Gini coefficient G =0.893 481
Balanced inequality ratio P =0.106 224
Outdegree balanced inequality ratio P+ =0.096 917 3
Indegree balanced inequality ratio P =0.139 232
Relative edge distribution entropy Her =0.800 928
Power law exponent γ =2.516 16
Tail power law exponent γt =2.411 00
Tail power law exponent with p γ3 =2.411 00
p-value p =0.000 00
Outdegree tail power law exponent with p γ3,o =2.011 00
Outdegree p-value po =0.306 000
Indegree tail power law exponent with p γ3,i =2.611 00
Indegree p-value pi =0.018 000 0
Degree assortativity ρ =−0.486 059
Degree assortativity p-value pρ =5.881 53 × 10−275
In/outdegree correlation ρ± =+0.640 704
Clustering coefficient c =0.041 039 2
Directed clustering coefficient c± =0.128 357
Spectral norm α =1,597.83
Operator 2-norm ν =841.843
Cyclic eigenvalue π =747.668
Algebraic connectivity a =0.115 155
Spectral separation 1[A] / λ2[A]| =1.477 97
Reciprocity y =0.334 881
Non-bipartivity bA =0.822 255
Normalized non-bipartivity bN =0.080 055 5
Algebraic non-bipartivity χ =0.196 562
Spectral bipartite frustration bK =0.009 903 76
Controllability C =759
Relative controllability Cr =0.642 676


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

In/outdegree scatter plot

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree 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] Jun Sun, Jérôme Kunegis, and Steffen Staab. Predicting user roles in social networks using transfer learning with feature transformation. In Proc. ICDM Workshop on Data Min. in Netw., 2016.