Wikipedia talk (nds)
This is the communication network of the Low German 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 =  23,132

Volume  m =  27,432

Unique edge count  m̿ =  24,668

Loop count  l =  1,525

Wedge count  s =  254,797,256

Claw count  z =  1,922,100,442,042

Cross count  x =  10,855,404,680,187,870

Triangle count  t =  739

Square count  q =  95,989

4Tour count  T_{4} =  1,020,005,236

Maximum degree  d_{max} =  22,612

Maximum outdegree  d^{+}_{max} =  22,563

Maximum indegree  d^{−}_{max} =  962

Average degree  d =  2.371 78

Fill  p =  4.610 07 × 10^{−5}

Average edge multiplicity  m̃ =  1.112 05

Size of LCC  N =  23,050

Size of LSCC  N_{s} =  155

Relative size of LSCC  N^{r}_{s} =  0.006 700 67

Diameter  δ =  6

50Percentile effective diameter  δ_{0.5} =  1.511 43

90Percentile effective diameter  δ_{0.9} =  1.920 69

Median distance  δ_{M} =  2

Mean distance  δ_{m} =  2.025 14

Gini coefficient  G =  0.576 368

Balanced inequality ratio  P =  0.295 130

Outdegree balanced inequality ratio  P_{+} =  0.044 036 2

Indegree balanced inequality ratio  P_{−} =  0.454 579

Relative edge distribution entropy  H_{er} =  0.595 034

Power law exponent  γ =  24.251 7

Tail power law exponent  γ_{t} =  4.461 00

Tail power law exponent with p  γ_{3} =  4.461 00

pvalue  p =  0.000 00

Outdegree tail power law exponent with p  γ_{3,o} =  2.401 00

Outdegree pvalue  p_{o} =  0.000 00

Indegree tail power law exponent with p  γ_{3,i} =  4.691 00

Indegree pvalue  p_{i} =  0.000 00

Degree assortativity  ρ =  −0.879 405

Degree assortativity pvalue  p_{ρ} =  0.000 00

In/outdegree correlation  ρ^{±} =  +0.546 548

Clustering coefficient  c =  8.701 04 × 10^{−6}

Directed clustering coefficient  c^{±} =  0.001 506 22

Spectral norm  α =  794.828

Operator 2norm  ν =  402.071

Cyclic eigenvalue  π =  391.973

Algebraic connectivity  a =  0.238 550

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.886 61

Reciprocity  y =  0.025 012 2

Nonbipartivity  b_{A} =  0.810 433

Normalized nonbipartivity  b_{N} =  0.007 856 82

Algebraic nonbipartivity  χ =  0.017 531 0

Spectral bipartite frustration  b_{K} =  0.002 062 78

Controllability  C =  22,641

Relative controllability  C_{r} =  0.978 774

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.
