Wikipedia talk (sk)

This is the communication network of the Slovak 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

CodeTsk
Internal namewiki_talk_sk
NameWikipedia talk (sk)
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 =41,452
Volume m =131,884
Unique edge count m̿ =71,933
Loop count l =23,137
Wedge count s =299,445,255
Claw count z =1,426,419,333,688
Cross count x =5,768,558,037,351,131
Triangle count t =38,859
Square count q =55,938,666
4-Tour count T4 =1,645,425,904
Maximum degree dmax =19,084
Maximum outdegree d+max =19,084
Maximum indegree dmax =4,191
Average degree d =6.363 22
Fill p =4.186 37 × 10−5
Average edge multiplicity m̃ =1.833 43
Size of LCC N =41,076
Size of LSCC Ns =1,538
Relative size of LSCC Nrs =0.037 103 2
Diameter δ =8
50-Percentile effective diameter δ0.5 =3.061 71
90-Percentile effective diameter δ0.9 =3.814 29
Median distance δM =4
Mean distance δm =3.287 62
Gini coefficient G =0.797 885
Balanced inequality ratio P =0.179 040
Outdegree balanced inequality ratio P+ =0.072 821 6
Indegree balanced inequality ratio P =0.261 328
Relative edge distribution entropy Her =0.691 012
Power law exponent γ =3.865 94
Tail power law exponent γt =1.671 00
Tail power law exponent with p γ3 =1.671 00
p-value p =0.493 000
Outdegree tail power law exponent with p γ3,o =1.621 00
Outdegree p-value po =0.417 000
Indegree tail power law exponent with p γ3,i =2.211 00
Indegree p-value pi =0.950 000
Degree assortativity ρ =−0.449 397
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.000 389 310
Directed clustering coefficient c± =0.019 368 3
Spectral norm α =3,635.21
Operator 2-norm ν =1,825.23
Cyclic eigenvalue π =1,803.43
Algebraic connectivity a =0.108 020
Spectral separation 1[A] / λ2[A]| =1.319 57
Reciprocity y =0.086 816 9
Normalized non-bipartivity bN =0.022 163 5
Algebraic non-bipartivity χ =0.059 074 1
Controllability C =39,153
Relative controllability Cr =0.944 538

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

Double Laplacian graph drawing

Delaunay graph drawing

In/outdegree scatter plot

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree distribution

Temporal distribution

Diameter/density evolution

SynGraphy

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.