Wikipedia talk (ar)
This is the communication network of the Arabic 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 =  1,095,799

Volume  m =  1,913,103

Unique edge count  m̿ =  1,564,598

Wedge count  s =  440,266,628,953

Triangle count  t =  485,421

Square count  q =  8,084,744,426

4Tour count  T_{4} =  1,825,747,539,904

Maximum degree  d_{max} =  915,536

Maximum outdegree  d^{+}_{max} =  915,524

Maximum indegree  d^{−}_{max} =  8,478

Average degree  d =  3.491 70

Average edge multiplicity  m̃ =  1.222 74

Size of LCC  N =  1,095,524

Diameter  δ =  6

50Percentile effective diameter  δ_{0.5} =  1.687 34

90Percentile effective diameter  δ_{0.9} =  3.472 25

Median distance  δ_{M} =  2

Mean distance  δ_{m} =  2.462 04

Gini coefficient  G =  0.688 965

Balanced inequality ratio  P =  0.228 882

Outdegree balanced inequality ratio  P_{+} =  0.029 937 2

Indegree balanced inequality ratio  P_{−} =  0.363 699

Tail power law exponent  γ_{t} =  1.731 00

Degree assortativity  ρ =  −0.486 706

Degree assortativity pvalue  p_{ρ} =  0.000 00

In/outdegree correlation  ρ^{±} =  +0.433 959

Clustering coefficient  c =  3.307 68 × 10^{−6}

Directed clustering coefficient  c^{±} =  0.038 936 4

Spectral norm  α =  7,061.88

Operator 2norm  ν =  3,550.07

Cyclic eigenvalue  π =  3,512.76

Algebraic connectivity  a =  0.014 483 7

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.682 92

Nonbipartivity  b_{A} =  0.742 158

Normalized nonbipartivity  b_{N} =  0.008 921 28

Algebraic nonbipartivity  χ =  0.024 587 1

Spectral bipartite frustration  b_{K} =  0.002 173 87

Controllability  C =  1,079,778

Relative controllability  C_{r} =  0.985 380

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.
