Wikipedia talk (el)
This is the communication network of the Greek 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 =  40,254

Volume  m =  190,279

Unique edge count  m̿ =  77,390

Loop count  l =  26,135

Wedge count  s =  181,858,756

Claw count  z =  581,119,219,524

Cross count  x =  1,536,422,106,910,311

Triangle count  t =  53,432

Square count  q =  66,577,096

4Tour count  T_{4} =  1,260,190,222

Maximum degree  d_{max} =  23,615

Maximum outdegree  d^{+}_{max} =  23,614

Maximum indegree  d^{−}_{max} =  6,251

Average degree  d =  9.453 92

Fill  p =  4.776 03 × 10^{−5}

Average edge multiplicity  m̃ =  2.458 70

Size of LCC  N =  39,667

Size of LSCC  N_{s} =  2,421

Relative size of LSCC  N^{r}_{s} =  0.060 143 1

Diameter  δ =  8

50Percentile effective diameter  δ_{0.5} =  2.729 31

90Percentile effective diameter  δ_{0.9} =  3.726 31

Median distance  δ_{M} =  3

Mean distance  δ_{m} =  3.232 07

Gini coefficient  G =  0.841 997

Balanced inequality ratio  P =  0.153 645

Outdegree balanced inequality ratio  P_{+} =  0.075 005 6

Indegree balanced inequality ratio  P_{−} =  0.239 065

Relative edge distribution entropy  H_{er} =  0.718 771

Power law exponent  γ =  3.419 32

Tail power law exponent  γ_{t} =  3.341 00

Tail power law exponent with p  γ_{3} =  3.341 00

pvalue  p =  0.000 00

Outdegree tail power law exponent with p  γ_{3,o} =  1.661 00

Outdegree pvalue  p_{o} =  0.432 000

Indegree tail power law exponent with p  γ_{3,i} =  3.491 00

Indegree pvalue  p_{i} =  0.000 00

Degree assortativity  ρ =  −0.385 175

Degree assortativity pvalue  p_{ρ} =  0.000 00

In/outdegree correlation  ρ^{±} =  +0.673 621

Clustering coefficient  c =  0.000 881 431

Directed clustering coefficient  c^{±} =  0.021 507 0

Spectral norm  α =  3,905.03

Operator 2norm  ν =  2,030.64

Cyclic eigenvalue  π =  1,889.59

Algebraic connectivity  a =  0.094 910 9

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.283 82

Reciprocity  y =  0.173 601

Nonbipartivity  b_{A} =  0.314 832

Normalized nonbipartivity  b_{N} =  0.021 515 7

Algebraic nonbipartivity  χ =  0.049 156 6

Spectral bipartite frustration  b_{K} =  0.003 406 57

Controllability  C =  37,016

Relative controllability  C_{r} =  0.919 561

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.
