Wikipedia talk (nds)

This is the communication network of the Low German 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_nds
NameWikipedia talk (nds)
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 =23,132
Volume m =27,432
Unique edge count m̿ =24,668
Loop count l =1,525
Wedge count s =254,797,256
Claw count z =1,922,100,442,042
Cross count x =10,855,404,680,187,870
Triangle count t =739
Square count q =95,989
4-Tour count T4 =1,020,005,236
Maximum degree dmax =22,612
Maximum outdegree d+max =22,563
Maximum indegree dmax =962
Average degree d =2.371 78
Fill p =4.610 07 × 10−5
Average edge multiplicity m̃ =1.112 05
Size of LCC N =23,050
Size of LSCC Ns =155
Relative size of LSCC Nrs =0.006 700 67
Diameter δ =6
50-Percentile effective diameter δ0.5 =1.511 43
90-Percentile effective diameter δ0.9 =1.920 69
Median distance δM =2
Mean distance δm =2.025 14
Gini coefficient G =0.576 368
Balanced inequality ratio P =0.295 130
Outdegree balanced inequality ratio P+ =0.044 036 2
Indegree balanced inequality ratio P =0.454 579
Relative edge distribution entropy Her =0.595 034
Power law exponent γ =24.251 7
Tail power law exponent γt =4.461 00
Tail power law exponent with p γ3 =4.461 00
p-value p =0.000 00
Outdegree tail power law exponent with p γ3,o =2.401 00
Outdegree p-value po =0.001 000 00
Indegree tail power law exponent with p γ3,i =4.691 00
Indegree p-value pi =0.000 00
Degree assortativity ρ =−0.879 405
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.546 548
Clustering coefficient c =8.701 04 × 10−6
Directed clustering coefficient c± =0.001 506 22
Spectral norm α =794.828
Operator 2-norm ν =402.071
Algebraic connectivity a =0.238 550
Spectral separation 1[A] / λ2[A]| =1.886 61
Reciprocity y =0.025 012 2
Algebraic non-bipartivity χ =0.017 531 0
Spectral bipartite frustration bK =0.002 062 78
Controllability C =22,641
Relative controllability Cr =0.978 774


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.