Wikipedia talk (ht)

This is the communication network of the Haitian Creole 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_ht
NameWikipedia talk (ht)
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 =536
Volume m =1,530
Unique edge count m̿ =957
Loop count l =340
Wedge count s =44,179
Claw count z =2,482,717
Cross count x =107,625,840
Triangle count t =125
Square count q =13,587
4-Tour count T4 =286,928
Maximum degree dmax =556
Maximum outdegree d+max =349
Maximum indegree dmax =207
Average degree d =5.708 96
Fill p =0.003 331 06
Average edge multiplicity m̃ =1.598 75
Size of LCC N =404
Size of LSCC Ns =26
Relative size of LSCC Nrs =0.048 507 5
Diameter δ =9
50-Percentile effective diameter δ0.5 =2.999 01
90-Percentile effective diameter δ0.9 =5.384 06
Median distance δM =3
Mean distance δm =3.582 82
Gini coefficient G =0.649 876
Relative edge distribution entropy Her =0.824 732
Power law exponent γ =2.819 03
Tail power law exponent γt =2.931 00
Degree assortativity ρ =−0.573 252
Degree assortativity p-value pρ =3.708 60 × 10−133
In/outdegree correlation ρ± =+0.102 919
Clustering coefficient c =0.008 488 20
Directed clustering coefficient c± =0.024 781 2
Spectral norm α =209.462
Operator 2-norm ν =107.190
Cyclic eigenvalue π =101.931
Algebraic connectivity a =0.044 988 7
Reciprocity y =0.238 245
Non-bipartivity bA =0.872 451
Normalized non-bipartivity bN =0.044 942 6
Algebraic non-bipartivity χ =0.194 136
Spectral bipartite frustration bK =0.012 301 0


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


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.