Wikipedia talk (ca)
This is the communication network of the Catalan 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 =  79,736

Volume  m =  351,610

Unique edge count  m̿ =  196,370

Loop count  l =  25,522

Wedge count  s =  1,950,651,151

Claw count  z =  28,268,646,448,345

Cross count  x =  349,599,213,028,924,544

Triangle count  t =  232,970

Square count  q =  359,935,734

4Tour count  T_{4} =  10,682,453,550

Maximum degree  d_{max} =  54,776

Maximum outdegree  d^{+}_{max} =  54,770

Maximum indegree  d^{−}_{max} =  11,424

Average degree  d =  8.819 35

Fill  p =  3.088 63 × 10^{−5}

Average edge multiplicity  m̃ =  1.790 55

Size of LCC  N =  79,209

Size of LSCC  N_{s} =  4,601

Relative size of LSCC  N^{r}_{s} =  0.057 702 9

Diameter  δ =  6

50Percentile effective diameter  δ_{0.5} =  2.068 45

90Percentile effective diameter  δ_{0.9} =  2.896 98

Median distance  δ_{M} =  3

Mean distance  δ_{m} =  2.583 28

Gini coefficient  G =  0.800 285

Balanced inequality ratio  P =  0.173 434

Outdegree balanced inequality ratio  P_{+} =  0.064 526 0

Indegree balanced inequality ratio  P_{−} =  0.274 426

Relative edge distribution entropy  H_{er} =  0.694 054

Power law exponent  γ =  2.458 35

Tail power law exponent  γ_{t} =  3.371 00

Tail power law exponent with p  γ_{3} =  3.371 00

pvalue  p =  0.000 00

Outdegree tail power law exponent with p  γ_{3,o} =  2.051 00

Outdegree pvalue  p_{o} =  0.000 00

Indegree tail power law exponent with p  γ_{3,i} =  2.131 00

Indegree pvalue  p_{i} =  0.342 000

Degree assortativity  ρ =  −0.334 579

Degree assortativity pvalue  p_{ρ} =  0.000 00

In/outdegree correlation  ρ^{±} =  +0.672 676

Clustering coefficient  c =  0.000 358 296

Directed clustering coefficient  c^{±} =  0.010 717 6

Spectral norm  α =  5,171.02

Operator 2norm  ν =  2,865.14

Cyclic eigenvalue  π =  2,544.51

Algebraic connectivity  a =  0.179 725

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.603 12

Reciprocity  y =  0.130 402

Nonbipartivity  b_{A} =  0.487 061

Normalized nonbipartivity  b_{N} =  0.036 461 6

Algebraic nonbipartivity  χ =  0.145 677

Spectral bipartite frustration  b_{K} =  0.007 793 52

Controllability  C =  74,939

Relative controllability  C_{r} =  0.939 839

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.
