Wikipedia talk (gl)

This is the communication network of the Galician 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_gl
NameWikipedia talk (gl)
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 =8,097
Volume m =63,809
Unique edge count m̿ =23,279
Loop count l =3,955
Wedge count s =18,550,063
Claw count z =22,382,348,663
Cross count x =23,425,727,965,064
Triangle count t =22,837
Square count q =5,022,119
4-Tour count T4 =114,416,978
Maximum degree dmax =5,815
Maximum outdegree d+max =5,815
Maximum indegree dmax =2,143
Average degree d =15.761 1
Fill p =0.000 355 072
Average edge multiplicity m̃ =2.741 05
Size of LCC N =7,920
Size of LSCC Ns =1,009
Relative size of LSCC Nrs =0.124 614
Diameter δ =7
50-Percentile effective diameter δ0.5 =2.121 48
90-Percentile effective diameter δ0.9 =2.990 63
Median distance δM =3
Mean distance δm =2.652 91
Gini coefficient G =0.856 243
Balanced inequality ratio P =0.136 258
Outdegree balanced inequality ratio P+ =0.077 575 3
Indegree balanced inequality ratio P =0.209 422
Relative edge distribution entropy Her =0.738 498
Power law exponent γ =2.251 76
Tail power law exponent γt =3.071 00
Tail power law exponent with p γ3 =3.071 00
p-value p =0.000 00
Outdegree tail power law exponent with p γ3,o =1.951 00
Outdegree p-value po =0.000 00
Indegree tail power law exponent with p γ3,i =3.201 00
Indegree p-value pi =0.000 00
Degree assortativity ρ =−0.359 948
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.709 225
Clustering coefficient c =0.003 693 30
Directed clustering coefficient c± =0.047 796 5
Spectral norm α =1,899.32
Operator 2-norm ν =984.015
Cyclic eigenvalue π =916.311
Algebraic connectivity a =0.154 410
Spectral separation 1[A] / λ2[A]| =1.647 82
Reciprocity y =0.253 834
Non-bipartivity bA =0.726 009
Normalized non-bipartivity bN =0.064 314 9
Algebraic non-bipartivity χ =0.297 571
Spectral bipartite frustration bK =0.014 311 8
Controllability C =7,045
Relative controllability Cr =0.870 075


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.