Wikipedia talk (nl)

This is the communication network of the Dutch 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_nl
NameWikipedia talk (nl)
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 =225,749
Volume m =1,554,699
Unique edge count m̿ =565,477
Loop count l =416,428
Wedge count s =6,058,956,301
Triangle count t =1,873,710
Square count q =1,925,439,105
4-Tour count T4 =39,640,359,028
Maximum degree dmax =113,872
Maximum outdegree d+max =113,867
Maximum indegree dmax =36,413
Average degree d =13.773 7
Average edge multiplicity m̃ =2.749 36
Size of LCC N =224,185
Diameter δ =7
50-Percentile effective diameter δ0.5 =2.601 71
90-Percentile effective diameter δ0.9 =3.637 83
Median distance δM =3
Mean distance δm =3.116 36
Degree assortativity ρ =−0.284 167
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.680 567
Clustering coefficient c =0.000 927 739
Operator 2-norm ν =19,257.0
Algebraic connectivity a =0.046 493 8
Normalized non-bipartivity bN =0.020 941 1
Algebraic non-bipartivity χ =0.043 211 4
Spectral bipartite frustration bK =0.002 293 82


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

Delaunay graph drawing

In/outdegree scatter plot

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree distribution

Temporal distribution

Diameter/density evolution


Inter-event distribution

Node-level inter-event distribution

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.