Wikipedia talk (pt)

This is the communication network of the Portuguese 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

CodeTpt
Internal namewiki_talk_pt
NameWikipedia talk (pt)
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 =541,355
Volume m =2,424,962
Unique edge count m̿ =1,463,308
Loop count l =153,601
Wedge count s =75,046,674,318
Claw count z =6,921,495,573,201,649
Cross count x =5.518 44 × 1020
Triangle count t =2,241,409
Square count q =13,588,648,746
4-Tour count T4 =408,898,646,862
Maximum degree dmax =504,444
Maximum outdegree d+max =504,376
Maximum indegree dmax =12,998
Average degree d =8.958 86
Fill p =4.993 11 × 10−6
Average edge multiplicity m̃ =1.657 18
Size of LCC N =534,618
Size of LSCC Ns =21,747
Relative size of LSCC Nrs =0.040 171 4
Diameter δ =9
50-Percentile effective diameter δ0.5 =2.224 04
90-Percentile effective diameter δ0.9 =3.247 04
Median distance δM =3
Mean distance δm =2.739 06
Gini coefficient G =0.762 874
Balanced inequality ratio P =0.201 567
Outdegree balanced inequality ratio P+ =0.049 860 6
Indegree balanced inequality ratio P =0.306 837
Relative edge distribution entropy Her =0.703 861
Power law exponent γ =2.261 13
Tail power law exponent γt =1.661 00
Degree assortativity ρ =−0.244 805
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.513 708
Clustering coefficient c =8.960 06 × 10−5
Directed clustering coefficient c± =0.014 204 0
Spectral norm α =6,906.26
Operator 2-norm ν =3,595.95
Cyclic eigenvalue π =3,342.92
Algebraic connectivity a =0.020 264 3
Spectral separation 1[A] / λ2[A]| =1.145 61
Reciprocity y =0.095 013 5
Non-bipartivity bA =0.746 785
Normalized non-bipartivity bN =0.014 708 2
Algebraic non-bipartivity χ =0.031 297 6
Spectral bipartite frustration bK =0.001 492 80
Controllability C =510,597
Relative controllability Cr =0.943 183

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.