Wikipedia talk (eu)

This is the communication network of the Basque 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

CodeTeu
Internal namewiki_talk_eu
NameWikipedia talk (eu)
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 =40,993
Volume m =58,120
Unique edge count m̿ =46,524
Loop count l =2,423
Wedge count s =452,599,778
Claw count z =4,296,627,707,315
Cross count x =31,495,070,549,626,512
Triangle count t =4,163
Square count q =688,013
4-Tour count T4 =1,815,993,208
Maximum degree dmax =29,480
Maximum outdegree d+max =29,478
Maximum indegree dmax =1,612
Average degree d =2.835 61
Fill p =2.768 58 × 10−5
Average edge multiplicity m̃ =1.249 25
Size of LCC N =40,854
Size of LSCC Ns =617
Relative size of LSCC Nrs =0.015 051 4
Diameter δ =7
50-Percentile effective diameter δ0.5 =1.918 56
90-Percentile effective diameter δ0.9 =3.734 08
Median distance δM =2
Mean distance δm =2.828 47
Gini coefficient G =0.643 616
Balanced inequality ratio P =0.259 196
Outdegree balanced inequality ratio P+ =0.059 446 0
Indegree balanced inequality ratio P =0.411 906
Relative edge distribution entropy Her =0.640 929
Power law exponent γ =16.004 6
Tail power law exponent γt =3.951 00
Tail power law exponent with p γ3 =3.951 00
p-value p =0.000 00
Outdegree tail power law exponent with p γ3,o =2.081 00
Outdegree p-value po =0.001 000 00
Indegree tail power law exponent with p γ3,i =4.131 00
Indegree p-value pi =0.000 00
Degree assortativity ρ =−0.546 059
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =2.759 39 × 10−5
Directed clustering coefficient c± =0.017 571 5
Spectral norm α =926.778
Operator 2-norm ν =473.976
Cyclic eigenvalue π =452.904
Spectral separation 1[A] / λ2[A]| =2.178 13
Reciprocity y =0.050 920 0
Normalized non-bipartivity bN =0.003 538 48
Algebraic non-bipartivity χ =0.007 430 54
Controllability C =40,135
Relative controllability Cr =0.979 070

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

Temporal hop 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.