Wikipedia talk (el)

This is the communication network of the Greek 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

CodeTel
Internal namewiki_talk_el
NameWikipedia talk (el)
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 =40,254
Volume m =190,279
Unique edge count m̿ =77,390
Loop count l =26,135
Wedge count s =181,858,756
Claw count z =581,119,219,524
Cross count x =1,536,422,106,910,311
Triangle count t =53,432
Square count q =66,577,096
4-Tour count T4 =1,260,190,222
Maximum degree dmax =23,615
Maximum outdegree d+max =23,614
Maximum indegree dmax =6,251
Average degree d =9.453 92
Fill p =4.776 03 × 10−5
Average edge multiplicity m̃ =2.458 70
Size of LCC N =39,667
Size of LSCC Ns =2,421
Relative size of LSCC Nrs =0.060 143 1
Diameter δ =8
50-Percentile effective diameter δ0.5 =2.729 31
90-Percentile effective diameter δ0.9 =3.726 31
Median distance δM =3
Mean distance δm =3.232 07
Gini coefficient G =0.841 997
Balanced inequality ratio P =0.153 645
Outdegree balanced inequality ratio P+ =0.075 005 6
Indegree balanced inequality ratio P =0.239 065
Relative edge distribution entropy Her =0.718 771
Power law exponent γ =3.419 32
Tail power law exponent γt =3.341 00
Tail power law exponent with p γ3 =3.341 00
p-value p =0.000 00
Outdegree tail power law exponent with p γ3,o =1.661 00
Outdegree p-value po =0.449 000
Indegree tail power law exponent with p γ3,i =3.491 00
Indegree p-value pi =0.000 00
Degree assortativity ρ =−0.385 175
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.673 621
Clustering coefficient c =0.000 881 431
Directed clustering coefficient c± =0.021 507 0
Spectral norm α =3,905.03
Operator 2-norm ν =2,030.64
Cyclic eigenvalue π =1,889.59
Algebraic connectivity a =0.094 910 9
Spectral separation 1[A] / λ2[A]| =1.283 82
Reciprocity y =0.173 601
Non-bipartivity bA =0.314 832
Normalized non-bipartivity bN =0.021 515 7
Algebraic non-bipartivity χ =0.049 156 6
Spectral bipartite frustration bK =0.003 406 57
Controllability C =37,016
Relative controllability Cr =0.919 561

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

Double Laplacian graph drawing

Delaunay graph drawing

In/outdegree scatter plot

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree distribution

Temporal distribution

Temporal hop 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.