Wikipedia talk (sv)

This is the communication network of the Swedish 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_sv
NameWikipedia talk (sv)
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 =120,833
Volume m =598,066
Unique edge count m̿ =261,494
Loop count l =145,000
Wedge count s =1,716,076,564
Claw count z =19,545,287,753,562
Triangle count t =401,802
Square count q =780,631,206
4-Tour count T4 =13,109,831,006
Maximum degree dmax =77,916
Maximum outdegree d+max =77,915
Maximum indegree dmax =6,261
Average degree d =9.899 05
Average edge multiplicity m̃ =2.287 11
Size of LCC N =119,327
Size of LSCC Ns =6,752
Relative size of LSCC Nrs =0.055 878 8
Diameter δ =8
50-Percentile effective diameter δ0.5 =2.603 98
90-Percentile effective diameter δ0.9 =3.629 07
Median distance δM =3
Mean distance δm =3.123 74
Gini coefficient G =0.835 312
Balanced inequality ratio P =0.159 254
Outdegree balanced inequality ratio P+ =0.075 618 7
Indegree balanced inequality ratio P =0.246 327
Relative edge distribution entropy Her =0.730 291
Power law exponent γ =3.039 23
Tail power law exponent γt =1.721 00
Tail power law exponent with p γ3 =1.721 00
p-value p =0.004 000 00
Outdegree tail power law exponent with p γ3,o =1.601 00
Outdegree p-value po =0.002 000 00
Indegree tail power law exponent with p γ3,i =2.081 00
Indegree p-value pi =0.000 00
Degree assortativity ρ =−0.268 996
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.673 375
Clustering coefficient c =0.000 702 420
Directed clustering coefficient c± =0.022 286 4
Spectral norm α =5,777.92
Cyclic eigenvalue π =2,747.71
Algebraic connectivity a =0.146 200
Non-bipartivity bA =0.827 077
Normalized non-bipartivity bN =0.042 919 5
Algebraic non-bipartivity χ =0.166 034
Spectral bipartite frustration bK =0.009 971 48
Controllability C =107,592
Relative controllability Cr =0.890 419


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

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.