Wikipedia talk (ht)
This is the communication network of the Haitian Creole 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 =  536

Volume  m =  1,530

Unique edge count  m̿ =  957

Loop count  l =  340

Wedge count  s =  44,179

Claw count  z =  2,482,717

Cross count  x =  107,625,840

Triangle count  t =  125

Square count  q =  13,587

4Tour count  T_{4} =  286,928

Maximum degree  d_{max} =  556

Maximum outdegree  d^{+}_{max} =  349

Maximum indegree  d^{−}_{max} =  207

Average degree  d =  5.708 96

Fill  p =  0.003 331 06

Average edge multiplicity  m̃ =  1.598 75

Size of LCC  N =  404

Size of LSCC  N_{s} =  26

Relative size of LSCC  N^{r}_{s} =  0.048 507 5

Diameter  δ =  9

50Percentile effective diameter  δ_{0.5} =  2.999 01

90Percentile effective diameter  δ_{0.9} =  5.384 06

Median distance  δ_{M} =  3

Mean distance  δ_{m} =  3.582 82

Gini coefficient  G =  0.649 876

Balanced inequality ratio  P =  0.260 458

Outdegree balanced inequality ratio  P_{+} =  0.170 588

Indegree balanced inequality ratio  P_{−} =  0.325 490

Relative edge distribution entropy  H_{er} =  0.824 732

Power law exponent  γ =  2.819 03

Tail power law exponent  γ_{t} =  2.931 00

Tail power law exponent with p  γ_{3} =  2.931 00

pvalue  p =  0.015 000 0

Outdegree tail power law exponent with p  γ_{3,o} =  2.321 00

Outdegree pvalue  p_{o} =  0.009 000 00

Indegree tail power law exponent with p  γ_{3,i} =  3.441 00

Indegree pvalue  p_{i} =  0.379 000

Degree assortativity  ρ =  −0.573 252

Degree assortativity pvalue  p_{ρ} =  3.708 60 × 10^{−133}

In/outdegree correlation  ρ^{±} =  +0.102 919

Clustering coefficient  c =  0.008 488 20

Directed clustering coefficient  c^{±} =  0.024 781 2

Spectral norm  α =  209.462

Operator 2norm  ν =  107.190

Cyclic eigenvalue  π =  101.931

Algebraic connectivity  a =  0.044 988 7

Spectral separation  λ_{1}[A] / λ_{2}[A] =  5.221 95

Reciprocity  y =  0.238 245

Nonbipartivity  b_{A} =  0.872 451

Normalized nonbipartivity  b_{N} =  0.044 942 6

Algebraic nonbipartivity  χ =  0.194 136

Spectral bipartite frustration  b_{K} =  0.012 301 0

Controllability  C =  339

Relative controllability  C_{r} =  0.632 463

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.
