Wikipedia talk (es)

This is the communication network of the Spanish 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

CodeTes
Internal namewiki_talk_es
NameWikipedia talk (es)
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 =497,446
Volume m =2,702,879
Unique edge count m̿ =1,250,097
Loop count l =310,456
Wedge count s =36,007,011,621
Claw count z =2,134,034,493,823,165
Cross count x =9.841 05 × 1019
Triangle count t =2,521,702
Square count q =17,513,494,039
4-Tour count T4 =284,138,127,786
Maximum degree dmax =373,874
Maximum outdegree d+max =373,873
Maximum indegree dmax =23,514
Average degree d =10.867 0
Fill p =5.051 87 × 10−6
Average edge multiplicity m̃ =2.162 14
Size of LCC N =476,465
Size of LSCC Ns =42,144
Relative size of LSCC Nrs =0.084 720 8
Diameter δ =10
50-Percentile effective diameter δ0.5 =2.720 21
90-Percentile effective diameter δ0.9 =3.744 94
Median distance δM =3
Mean distance δm =3.223 73
Gini coefficient G =0.819 217
Balanced inequality ratio P =0.165 433
Outdegree balanced inequality ratio P+ =0.078 457 8
Indegree balanced inequality ratio P =0.262 955
Relative edge distribution entropy Her =0.732 569
Power law exponent γ =2.774 01
Tail power law exponent γt =1.781 00
Degree assortativity ρ =−0.234 987
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.638 269
Clustering coefficient c =0.000 210 101
Directed clustering coefficient c± =0.027 027 9
Spectral norm α =7,538.02
Operator 2-norm ν =3,800.41
Cyclic eigenvalue π =3,733.74
Algebraic connectivity a =0.042 304 4
Spectral separation 1[A] / λ2[A]| =1.162 15
Reciprocity y =0.250 041
Non-bipartivity bA =0.761 780
Normalized non-bipartivity bN =0.017 255 3
Algebraic non-bipartivity χ =0.043 537 3
Spectral bipartite frustration bK =0.002 352 25
Controllability C =433,355
Relative controllability Cr =0.871 160

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

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.