Wikipedia talk (fr)

This is the communication network of the French 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

CodeTfr
Internal namewiki_talk_fr
NameWikipedia talk (fr)
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 =1,420,367
Volume m =4,641,928
Unique edge count m̿ =2,471,501
Loop count l =788,289
Wedge count s =623,970,181,277
Claw count z =220,098,227,047,176,064
Cross count x =6.006 11 × 1022
Triangle count t =4,835,877
Square count q =11,749,091,667
4-Tour count T4 =2,589,877,992,672
Maximum degree dmax =1,096,752
Maximum outdegree d+max =1,096,720
Maximum indegree dmax =60,407
Average degree d =6.536 24
Fill p =1.225 07 × 10−6
Average edge multiplicity m̃ =1.878 18
Size of LCC N =1,409,540
Size of LSCC Ns =56,011
Relative size of LSCC Nrs =0.039 434 2
Diameter δ =11
50-Percentile effective diameter δ0.5 =1.798 42
90-Percentile effective diameter δ0.9 =3.541 98
Median distance δM =2
Mean distance δm =2.592 08
Gini coefficient G =0.825 557
Balanced inequality ratio P =0.153 816
Outdegree balanced inequality ratio P+ =0.062 284 7
Indegree balanced inequality ratio P =0.237 932
Relative edge distribution entropy Her =0.663 160
Power law exponent γ =4.884 49
Tail power law exponent γt =2.601 00
Degree assortativity ρ =−0.351 518
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.705 697
Clustering coefficient c =2.325 05 × 10−5
Directed clustering coefficient c± =0.025 045 3
Spectral norm α =110,219
Cyclic eigenvalue π =55,109.1
Algebraic connectivity a =0.024 088 0
Reciprocity y =0.139 799
Non-bipartivity bA =0.977 206
Normalized non-bipartivity bN =0.012 556 7
Spectral bipartite frustration bK =0.001 829 73
Controllability C =1,351,987
Relative controllability Cr =0.951 858

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

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.