Wikipedia talk (vi)

This is the communication network of the Vietnamese 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

CodeTvi
Internal namewiki_talk_vi
NameWikipedia talk (vi)
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 =338,714
Volume m =607,087
Unique edge count m̿ =426,086
Loop count l =43,802
Wedge count s =6,861,145,804
Claw count z =124,841,223,053,737
Cross count x =2,025,346,130,080,254,208
Triangle count t =138,848
Square count q =279,874,289
4-Tour count T4 =29,684,403,888
Maximum degree dmax =78,340
Maximum outdegree d+max =78,334
Maximum indegree dmax =8,085
Average degree d =3.584 66
Fill p =3.713 91 × 10−6
Average edge multiplicity m̃ =1.424 80
Size of LCC N =337,273
Size of LSCC Ns =2,785
Relative size of LSCC Nrs =0.008 222 28
Diameter δ =8
50-Percentile effective diameter δ0.5 =3.392 56
90-Percentile effective diameter δ0.9 =4.104 07
Median distance δM =4
Mean distance δm =3.731 68
Gini coefficient G =0.712 416
Balanced inequality ratio P =0.217 593
Outdegree balanced inequality ratio P+ =0.042 310 2
Indegree balanced inequality ratio P =0.356 992
Relative edge distribution entropy Her =0.677 591
Power law exponent γ =9.350 86
Tail power law exponent γt =3.321 00
Degree assortativity ρ =−0.437 310
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.592 984
Clustering coefficient c =6.071 06 × 10−5
Directed clustering coefficient c± =0.030 005 7
Spectral norm α =7,377.97
Operator 2-norm ν =3,917.74
Cyclic eigenvalue π =3,552.12
Algebraic connectivity a =0.022 289 2
Spectral separation 1[A] / λ2[A]| =2.170 98
Reciprocity y =0.046 903 7
Non-bipartivity bA =0.551 289
Normalized non-bipartivity bN =0.006 308 98
Algebraic non-bipartivity χ =0.014 534 6
Spectral bipartite frustration bK =0.001 467 47
Controllability C =332,293
Relative controllability Cr =0.981 043

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

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.