Wikipedia talk (de)

This is the communication network of the 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.

Metadata

CodeTde
Internal namewiki_talk_de
NameWikipedia talk (de)
Data sourcehttps://zenodo.org/record/49561
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Category
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

Statistics

Size n =519,403
Volume m =6,729,794
Unique edge count m̿ =1,751,343
Loop count l =2,174,035
Wedge count s =22,215,138,984
Claw count z =983,926,400,524,723
Cross count x =3.502 66 × 1019
Triangle count t =9,554,210
Square count q =12,355,986,906
4-Tour count T4 =187,711,464,036
Maximum degree dmax =395,780
Maximum outdegree d+max =395,774
Maximum indegree dmax =27,165
Average degree d =25.913 6
Fill p =6.491 76 × 10−6
Average edge multiplicity m̃ =3.842 65
Size of LCC N =505,468
Size of LSCC Ns =69,121
Relative size of LSCC Nrs =0.133 078
Diameter δ =13
50-Percentile effective diameter δ0.5 =2.780 91
90-Percentile effective diameter δ0.9 =3.746 41
Median distance δM =3
Mean distance δm =3.296 97
Gini coefficient G =0.916 814
Balanced inequality ratio P =0.092 355 7
Outdegree balanced inequality ratio P+ =0.076 872 0
Indegree balanced inequality ratio P =0.137 522
Relative edge distribution entropy Her =0.763 449
Power law exponent γ =2.554 68
Tail power law exponent γt =1.811 00
Degree assortativity ρ =−0.124 769
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.723 786
Clustering coefficient c =0.001 290 23
Directed clustering coefficient c± =0.034 430 7
Spectral norm α =22,742.4
Operator 2-norm ν =18,161.3
Cyclic eigenvalue π =10,171.3
Algebraic connectivity a =0.033 983 0
Spectral separation 1[A] / λ2[A]| =1.117 23
Reciprocity y =0.223 920
Non-bipartivity bA =0.342 587
Normalized non-bipartivity bN =0.018 009 7
Algebraic non-bipartivity χ =0.033 969 5
Spectral bipartite frustration bK =0.001 349 57
Controllability C =413,087
Relative controllability Cr =0.795 311

Plots

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

In/outdegree scatter plot

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree distribution

Temporal distribution

Diameter/density evolution

SynGraphy

Inter-event distribution

Node-level inter-event distribution

Matrix decompositions plots

Downloads

References

[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.