Wikipedia talk (cy)
This is the communication network of the Welsh 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 =  2,233

Volume  m =  10,740

Unique edge count  m̿ =  4,475

Loop count  l =  2,605

Wedge count  s =  772,894

Claw count  z =  211,757,981

Cross count  x =  44,162,305,687

Triangle count  t =  2,312

Square count  q =  147,394

4Tour count  T_{4} =  4,277,948

Maximum degree  d_{max} =  2,663

Maximum outdegree  d^{+}_{max} =  1,519

Maximum indegree  d^{−}_{max} =  1,144

Average degree  d =  9.619 35

Fill  p =  0.000 897 461

Average edge multiplicity  m̃ =  2.400 00

Size of LCC  N =  2,062

Size of LSCC  N_{s} =  249

Relative size of LSCC  N^{r}_{s} =  0.111 509

Diameter  δ =  9

50Percentile effective diameter  δ_{0.5} =  2.334 22

90Percentile effective diameter  δ_{0.9} =  2.949 30

Median distance  δ_{M} =  3

Mean distance  δ_{m} =  2.794 82

Gini coefficient  G =  0.821 236

Balanced inequality ratio  P =  0.163 175

Outdegree balanced inequality ratio  P_{+} =  0.126 816

Indegree balanced inequality ratio  P_{−} =  0.226 164

Relative edge distribution entropy  H_{er} =  0.771 327

Power law exponent  γ =  3.080 64

Tail power law exponent  γ_{t} =  2.731 00

Tail power law exponent with p  γ_{3} =  2.731 00

pvalue  p =  0.000 00

Outdegree tail power law exponent with p  γ_{3,o} =  2.141 00

Outdegree pvalue  p_{o} =  0.040 000 0

Indegree tail power law exponent with p  γ_{3,i} =  3.031 00

Indegree pvalue  p_{i} =  0.000 00

Degree assortativity  ρ =  −0.456 825

Degree assortativity pvalue  p_{ρ} =  0.000 00

In/outdegree correlation  ρ^{±} =  +0.611 678

Clustering coefficient  c =  0.008 974 06

Directed clustering coefficient  c^{±} =  0.029 888 5

Spectral norm  α =  1,034.97

Operator 2norm  ν =  527.246

Cyclic eigenvalue  π =  504.058

Algebraic connectivity  a =  0.069 279 6

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.921 85

Reciprocity  y =  0.269 497

Nonbipartivity  b_{A} =  0.945 995

Normalized nonbipartivity  b_{N} =  0.039 743 4

Algebraic nonbipartivity  χ =  0.068 781 7

Spectral bipartite frustration  b_{K} =  0.004 467 86

Controllability  C =  1,632

Relative controllability  C_{r} =  0.730 855

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.
