Wikipedia talk (ru)
This is the communication network of the Russian 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 =  457,017

Volume  m =  2,282,055

Unique edge count  m̿ =  919,790

Wedge count  s =  25,135,039,055

Triangle count  t =  1,825,612

Square count  q =  4,632,803,687

4Tour count  T_{4} =  137,604,283,760

Maximum degree  d_{max} =  188,103

Maximum outdegree  d^{+}_{max} =  188,102

Maximum indegree  d^{−}_{max} =  25,917

Average degree  d =  9.986 74

Average edge multiplicity  m̃ =  2.481 06

Size of LCC  N =  449,042

Diameter  δ =  8

50Percentile effective diameter  δ_{0.5} =  3.002 60

90Percentile effective diameter  δ_{0.9} =  3.807 86

Median distance  δ_{M} =  4

Mean distance  δ_{m} =  3.286 39

Power law exponent  γ =  3.649 44

Tail power law exponent  γ_{t} =  1.851 00

Degree assortativity  ρ =  −0.384 446

Degree assortativity pvalue  p_{ρ} =  0.000 00

Clustering coefficient  c =  0.000 217 896

Spectral norm  α =  16,905.3

Operator 2norm  ν =  8,527.66

Cyclic eigenvalue  π =  8,374.71

Algebraic connectivity  a =  0.087 733 7

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.606 98

Nonbipartivity  b_{A} =  0.926 913

Normalized nonbipartivity  b_{N} =  0.030 047 7

Algebraic nonbipartivity  χ =  0.087 219 0

Spectral bipartite frustration  b_{K} =  0.005 593 42

Controllability  C =  420,007

Relative controllability  C_{r} =  0.919 018

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.
