Wikipedia talk (ru)

This is the communication network of the Russian 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

CodeTru
Internal namewiki_talk_ru
NameWikipedia talk (ru)
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 =457,017
Volume m =2,282,055
Unique edge count m̿ =919,790
Loop count l =612,000
Wedge count s =25,135,039,055
Claw count z =893,229,694,932,742
Cross count x =2.497 17 × 1019
Triangle count t =1,825,612
Square count q =4,632,803,687
4-Tour count T4 =137,604,283,760
Maximum degree dmax =188,103
Maximum outdegree d+max =188,102
Maximum indegree dmax =25,917
Average degree d =9.986 74
Fill p =4.403 76 × 10−6
Average edge multiplicity m̃ =2.481 06
Size of LCC N =449,042
Size of LSCC Ns =22,664
Relative size of LSCC Nrs =0.049 591 2
Diameter δ =8
50-Percentile effective diameter δ0.5 =3.002 60
90-Percentile effective diameter δ0.9 =3.807 86
Median distance δM =4
Mean distance δm =3.286 39
Gini coefficient G =0.865 655
Balanced inequality ratio P =0.134 060
Outdegree balanced inequality ratio P+ =0.078 420 1
Indegree balanced inequality ratio P =0.208 729
Relative edge distribution entropy Her =0.703 860
Power law exponent γ =3.649 44
Tail power law exponent γt =1.851 00
Degree assortativity ρ =−0.384 446
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.701 125
Clustering coefficient c =0.000 217 896
Directed clustering coefficient c± =0.032 591 0
Spectral norm α =16,905.3
Operator 2-norm ν =8,527.66
Cyclic eigenvalue π =8,374.71
Algebraic connectivity a =0.087 733 7
Spectral separation 1[A] / λ2[A]| =1.606 98
Reciprocity y =0.116 706
Non-bipartivity bA =0.926 913
Normalized non-bipartivity bN =0.030 047 7
Algebraic non-bipartivity χ =0.087 219 0
Spectral bipartite frustration bK =0.005 593 42
Controllability C =420,007
Relative controllability Cr =0.919 018

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

Delaunay graph drawing

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.