Wikipedia talk (pt)
This is the communication network of the Portuguese 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 =  541,355

Volume  m =  2,424,962

Unique edge count  m̿ =  1,463,308

Wedge count  s =  75,046,674,318

Triangle count  t =  2,241,409

Square count  q =  13,588,648,746

4Tour count  T_{4} =  408,898,646,862

Maximum degree  d_{max} =  504,444

Maximum outdegree  d^{+}_{max} =  504,376

Maximum indegree  d^{−}_{max} =  12,998

Average degree  d =  8.958 86

Fill  p =  4.993 11 × 10^{−6}

Average edge multiplicity  m̃ =  1.657 18

Size of LCC  N =  534,618

Diameter  δ =  9

50Percentile effective diameter  δ_{0.5} =  2.224 04

90Percentile effective diameter  δ_{0.9} =  3.247 04

Median distance  δ_{M} =  3

Mean distance  δ_{m} =  2.739 06

Gini coefficient  G =  0.762 874

Balanced inequality ratio  P =  0.201 567

Outdegree balanced inequality ratio  P_{+} =  0.049 860 6

Indegree balanced inequality ratio  P_{−} =  0.306 837

Relative edge distribution entropy  H_{er} =  0.703 861

Power law exponent  γ =  2.261 13

Tail power law exponent  γ_{t} =  1.661 00

Degree assortativity  ρ =  −0.244 805

Degree assortativity pvalue  p_{ρ} =  0.000 00

Clustering coefficient  c =  8.960 06 × 10^{−5}

Directed clustering coefficient  c^{±} =  0.014 204 0

Spectral norm  α =  6,906.26

Operator 2norm  ν =  3,595.95

Cyclic eigenvalue  π =  3,342.92

Algebraic connectivity  a =  0.020 264 3

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.145 61

Nonbipartivity  b_{A} =  0.746 785

Normalized nonbipartivity  b_{N} =  0.014 708 2

Algebraic nonbipartivity  χ =  0.031 297 6

Spectral bipartite frustration  b_{K} =  0.001 492 80

Controllability  C =  510,597

Relative controllability  C_{r} =  0.943 183

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.
