Wikipedia talk (sr)

This is the communication network of the Serbian 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

CodeTsr
Internal namewiki_talk_sr
NameWikipedia talk (sr)
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 =103,068
Volume m =312,837
Unique edge count m̿ =153,042
Loop count l =17,756
Wedge count s =1,421,288,750
Claw count z =19,063,671,862,197
Cross count x =218,240,304,747,369,440
Triangle count t =103,105
Square count q =85,487,620
4-Tour count T4 =6,369,340,732
Maximum degree dmax =47,687
Maximum outdegree d+max =47,685
Maximum indegree dmax =9,057
Average degree d =6.070 50
Fill p =1.440 66 × 10−5
Average edge multiplicity m̃ =2.044 13
Size of LCC N =102,816
Size of LSCC Ns =2,416
Relative size of LSCC Nrs =0.023 440 8
Diameter δ =7
50-Percentile effective diameter δ0.5 =2.843 54
90-Percentile effective diameter δ0.9 =3.791 96
Median distance δM =3
Mean distance δm =3.219 08
Gini coefficient G =0.820 749
Balanced inequality ratio P =0.149 006
Outdegree balanced inequality ratio P+ =0.056 579 0
Indegree balanced inequality ratio P =0.246 863
Relative edge distribution entropy Her =0.681 060
Power law exponent γ =6.070 31
Tail power law exponent γt =2.831 00
Degree assortativity ρ =−0.330 145
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.683 829
Clustering coefficient c =0.000 217 630
Directed clustering coefficient c± =0.038 407 8
Spectral norm α =5,147.51
Operator 2-norm ν =2,609.00
Cyclic eigenvalue π =2,550.56
Algebraic connectivity a =0.043 711 9
Spectral separation 1[A] / λ2[A]| =1.589 35
Reciprocity y =0.121 365
Non-bipartivity bA =0.529 573
Normalized non-bipartivity bN =0.011 641 0
Algebraic non-bipartivity χ =0.024 720 5
Spectral bipartite frustration bK =0.002 193 01
Controllability C =99,966
Relative controllability Cr =0.969 903

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.