Wikipedia talk (zh)

This is the communication network of the Chinese 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

CodeTzh
Internal namewiki_talk_zh
NameWikipedia talk (zh)
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 =1,219,241
Volume m =2,284,546
Unique edge count m̿ =1,735,118
Loop count l =110,689
Wedge count s =454,139,500,995
Claw count z =137,646,405,415,475,008
Cross count x =3.215 48 × 1022
Triangle count t =1,266,904
Square count q =6,675,967,773
4-Tour count T4 =1,869,969,123,320
Maximum degree dmax =937,210
Maximum outdegree d+max =937,208
Maximum indegree dmax =9,268
Average degree d =3.747 49
Fill p =1.167 21 × 10−6
Average edge multiplicity m̃ =1.316 65
Size of LCC N =1,217,365
Size of LSCC Ns =10,831
Relative size of LSCC Nrs =0.008 883 40
Diameter δ =8
50-Percentile effective diameter δ0.5 =1.822 08
90-Percentile effective diameter δ0.9 =3.716 31
Median distance δM =2
Mean distance δm =2.742 44
Gini coefficient G =0.713 955
Balanced inequality ratio P =0.209 474
Outdegree balanced inequality ratio P+ =0.044 899 5
Indegree balanced inequality ratio P =0.346 666
Relative edge distribution entropy Her =0.637 871
Power law exponent γ =6.217 44
Tail power law exponent γt =2.861 00
Degree assortativity ρ =−0.420 015
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.545 043
Clustering coefficient c =8.369 04 × 10−6
Directed clustering coefficient c± =0.021 421 7
Spectral norm α =4,652.72
Operator 2-norm ν =3,132.92
Cyclic eigenvalue π =2,323.87
Algebraic connectivity a =0.037 007 8
Spectral separation 1[A] / λ2[A]| =1.425 91
Reciprocity y =0.044 536 5
Non-bipartivity bA =0.352 994
Normalized non-bipartivity bN =0.012 765 2
Algebraic non-bipartivity χ =0.029 891 8
Spectral bipartite frustration bK =0.002 671 72
Controllability C =1,201,258
Relative controllability Cr =0.985 251

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.