Wikipedia talk (ca)

This is the communication network of the Catalan 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_ca
NameWikipedia talk (ca)
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 =79,736
Volume m =351,610
Unique edge count m̿ =196,370
Loop count l =25,522
Wedge count s =1,950,651,151
Claw count z =28,268,646,448,345
Cross count x =349,599,213,028,924,544
Triangle count t =232,970
Square count q =359,935,734
4-Tour count T4 =10,682,453,550
Maximum degree dmax =54,776
Maximum outdegree d+max =54,770
Maximum indegree dmax =11,424
Average degree d =8.819 35
Fill p =3.088 63 × 10−5
Average edge multiplicity m̃ =1.790 55
Size of LCC N =79,209
Size of LSCC Ns =4,601
Relative size of LSCC Nrs =0.057 702 9
Diameter δ =6
50-Percentile effective diameter δ0.5 =2.068 45
90-Percentile effective diameter δ0.9 =2.896 98
Median distance δM =3
Mean distance δm =2.583 28
Gini coefficient G =0.800 285
Balanced inequality ratio P =0.173 434
Outdegree balanced inequality ratio P+ =0.064 526 0
Indegree balanced inequality ratio P =0.274 426
Relative edge distribution entropy Her =0.694 054
Power law exponent γ =2.458 35
Tail power law exponent γt =3.371 00
Tail power law exponent with p γ3 =3.371 00
p-value p =0.000 00
Outdegree tail power law exponent with p γ3,o =2.051 00
Outdegree p-value po =0.000 00
Indegree tail power law exponent with p γ3,i =2.131 00
Indegree p-value pi =0.360 000
Degree assortativity ρ =−0.334 579
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.672 676
Clustering coefficient c =0.000 358 296
Directed clustering coefficient c± =0.010 717 6
Spectral norm α =5,171.02
Operator 2-norm ν =2,865.14
Cyclic eigenvalue π =2,544.51
Algebraic connectivity a =0.179 725
Spectral separation 1[A] / λ2[A]| =1.603 12
Reciprocity y =0.130 402
Non-bipartivity bA =0.487 061
Normalized non-bipartivity bN =0.036 461 6
Algebraic non-bipartivity χ =0.145 677
Spectral bipartite frustration bK =0.007 793 52
Controllability C =74,939
Relative controllability Cr =0.939 839


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.