Wikipedia talk (oc)

This is the communication network of the Occitan 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.


Internal namewiki_talk_oc
NameWikipedia talk (oc)
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
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


Size n =3,144
Volume m =11,059
Unique edge count m̿ =4,835
Loop count l =1,188
Wedge count s =1,961,710
Claw count z =1,089,006,133
Cross count x =436,063,783,229
Triangle count t =1,531
Square count q =60,501
4-Tour count T4 =8,339,044
Maximum degree dmax =3,251
Maximum outdegree d+max =2,202
Maximum indegree dmax =1,049
Average degree d =7.034 99
Fill p =0.000 489 138
Average edge multiplicity m̃ =2.287 28
Size of LCC N =3,054
Size of LSCC Ns =283
Relative size of LSCC Nrs =0.090 012 7
Diameter δ =6
50-Percentile effective diameter δ0.5 =2.209 99
90-Percentile effective diameter δ0.9 =2.952 51
Median distance δM =3
Mean distance δm =2.687 79
Gini coefficient G =0.828 848
Balanced inequality ratio P =0.148 657
Outdegree balanced inequality ratio P+ =0.101 637
Indegree balanced inequality ratio P =0.219 188
Relative edge distribution entropy Her =0.720 240
Power law exponent γ =4.981 79
Tail power law exponent γt =2.621 00
Tail power law exponent with p γ3 =2.621 00
p-value p =0.000 00
Outdegree tail power law exponent with p γ3,o =2.141 00
Outdegree p-value po =0.007 000 00
Indegree tail power law exponent with p γ3,i =2.801 00
Indegree p-value pi =0.000 00
Degree assortativity ρ =−0.518 834
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.664 161
Clustering coefficient c =0.002 341 32
Directed clustering coefficient c± =0.013 204 6
Spectral norm α =684.900
Operator 2-norm ν =418.607
Cyclic eigenvalue π =277.690
Algebraic connectivity a =0.226 624
Spectral separation 1[A] / λ2[A]| =1.623 01
Reciprocity y =0.228 128
Non-bipartivity bA =0.597 879
Normalized non-bipartivity bN =0.055 316 2
Algebraic non-bipartivity χ =0.125 065
Spectral bipartite frustration bK =0.010 905 3
Controllability C =2,697
Relative controllability Cr =0.857 824


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


Matrix decompositions plots



[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.