Wikipedia talk (en)
This is the communication network of the English 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
Statistics
Size  n =  2,987,535

Volume  m =  24,981,163

Unique edge count  m̿ =  9,379,561

Loop count  l =  5,655,527

Wedge count  s =  57,066,712,805

Claw count  z =  1,510,569,161,023,742

Cross count  x =  4.396 49 × 10^{19}

Triangle count  t =  41,915,754

Square count  q =  22,498,726,804

Maximum degree  d_{max} =  488,182

Maximum outdegree  d^{+}_{max} =  488,169

Maximum indegree  d^{−}_{max} =  121,250

Average degree  d =  16.723 6

Fill  p =  1.050 89 × 10^{−6}

Average edge multiplicity  m̃ =  2.663 36

Size of LCC  N =  2,859,574

Size of LSCC  N_{s} =  249,610

Relative size of LSCC  N^{r}_{s} =  0.083 550 5

Diameter  δ =  9

50Percentile effective diameter  δ_{0.5} =  3.233 96

90Percentile effective diameter  δ_{0.9} =  3.877 48

Median distance  δ_{M} =  4

Mean distance  δ_{m} =  3.658 54

Gini coefficient  G =  0.899 562

Balanced inequality ratio  P =  0.109 987

Outdegree balanced inequality ratio  P_{+} =  0.073 885 3

Indegree balanced inequality ratio  P_{−} =  0.160 171

Relative edge distribution entropy  H_{er} =  0.785 090

Power law exponent  γ =  2.827 07

Tail power law exponent  γ_{t} =  1.811 00

Degree assortativity  ρ =  −0.096 231 0

Degree assortativity pvalue  p_{ρ} =  0.000 00

Clustering coefficient  c =  0.002 203 51

Directed clustering coefficient  c^{±} =  0.017 955 2

Spectral norm  α =  90,816.2

Operator 2norm  ν =  45,410.1

Cyclic eigenvalue  π =  45,406.0

Reciprocity  y =  0.214 524

Nonbipartivity  b_{A} =  0.829 994

Normalized nonbipartivity  b_{N} =  0.031 296 9

Algebraic nonbipartivity  χ =  0.064 187 3

Spectral bipartite frustration  b_{K} =  0.002 707 63

Plots
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.
