Wikipedia talk (zh)
This is the communication network of the Chinese 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 =  1,219,241

Volume  m =  2,284,546

Unique edge count  m̿ =  1,735,118

Wedge count  s =  454,139,500,995

Claw count  z =  137,646,405,415,475,008

Triangle count  t =  1,266,904

Square count  q =  6,675,967,773

4Tour count  T_{4} =  1,869,969,123,320

Maximum degree  d_{max} =  937,210

Maximum outdegree  d^{+}_{max} =  937,208

Maximum indegree  d^{−}_{max} =  9,268

Average degree  d =  3.747 49

Average edge multiplicity  m̃ =  1.316 65

Size of LCC  N =  1,217,365

Diameter  δ =  8

50Percentile effective diameter  δ_{0.5} =  1.822 08

90Percentile effective diameter  δ_{0.9} =  3.716 31

Median distance  δ_{M} =  2

Mean distance  δ_{m} =  2.742 44

Gini coefficient  G =  0.713 955

Balanced inequality ratio  P =  0.209 474

Outdegree balanced inequality ratio  P_{+} =  0.044 899 5

Indegree balanced inequality ratio  P_{−} =  0.346 666

Power law exponent  γ =  6.217 44

Tail power law exponent  γ_{t} =  2.861 00

Degree assortativity  ρ =  −0.420 015

Degree assortativity pvalue  p_{ρ} =  0.000 00

In/outdegree correlation  ρ^{±} =  +0.545 043

Clustering coefficient  c =  8.369 04 × 10^{−6}

Directed clustering coefficient  c^{±} =  0.021 421 7

Spectral norm  α =  4,652.72

Operator 2norm  ν =  3,132.92

Cyclic eigenvalue  π =  2,323.87

Algebraic connectivity  a =  0.037 007 8

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.425 91

Reciprocity  y =  0.044 536 5

Nonbipartivity  b_{A} =  0.352 994

Normalized nonbipartivity  b_{N} =  0.012 765 2

Algebraic nonbipartivity  χ =  0.029 891 8

Spectral bipartite frustration  b_{K} =  0.002 671 72

Controllability  C =  1,201,258

Relative controllability  C_{r} =  0.985 251

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.
