Wikipedia talk (ar)

This is the communication network of the Arabic 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

CodeTar
Internal namewiki_talk_ar
NameWikipedia talk (ar)
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 =1,095,799
Volume m =1,913,103
Unique edge count m̿ =1,564,598
Loop count l =73,297
Wedge count s =440,266,628,953
Claw count z =128,633,431,735,339,136
Cross count x =2.929 3 × 1022
Triangle count t =485,421
Square count q =8,084,744,426
4-Tour count T4 =1,825,747,539,904
Maximum degree dmax =915,536
Maximum outdegree d+max =915,524
Maximum indegree dmax =8,478
Average degree d =3.491 70
Fill p =1.302 99 × 10−6
Average edge multiplicity m̃ =1.222 74
Size of LCC N =1,095,524
Size of LSCC Ns =8,797
Relative size of LSCC Nrs =0.008 027 93
Diameter δ =6
50-Percentile effective diameter δ0.5 =1.687 34
90-Percentile effective diameter δ0.9 =3.472 25
Median distance δM =2
Mean distance δm =2.462 04
Gini coefficient G =0.688 965
Balanced inequality ratio P =0.228 882
Outdegree balanced inequality ratio P+ =0.029 937 2
Indegree balanced inequality ratio P =0.363 699
Relative edge distribution entropy Her =0.614 730
Power law exponent γ =5.396 97
Tail power law exponent γt =1.731 00
Degree assortativity ρ =−0.486 706
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.433 959
Clustering coefficient c =3.307 68 × 10−6
Directed clustering coefficient c± =0.038 936 4
Spectral norm α =7,061.88
Operator 2-norm ν =3,550.07
Cyclic eigenvalue π =3,512.76
Algebraic connectivity a =0.014 483 7
Spectral separation 1[A] / λ2[A]| =1.682 92
Reciprocity y =0.029 236 3
Non-bipartivity bA =0.742 158
Normalized non-bipartivity bN =0.008 921 28
Algebraic non-bipartivity χ =0.024 587 1
Spectral bipartite frustration bK =0.002 173 87
Controllability C =1,079,778
Relative controllability Cr =0.985 380

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.