Wikipedia talk (ru)
This is the communication network of the Russian 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 = | 457,017
|
Volume | m = | 2,282,055
|
Unique edge count | m̿ = | 919,790
|
Wedge count | s = | 25,135,039,055
|
Claw count | z = | 893,229,694,932,742
|
Cross count | x = | 2.497 17 × 1019
|
Triangle count | t = | 1,825,612
|
Square count | q = | 4,632,803,687
|
4-Tour count | T4 = | 137,604,283,760
|
Maximum degree | dmax = | 188,103
|
Maximum outdegree | d+max = | 188,102
|
Maximum indegree | d−max = | 25,917
|
Average degree | d = | 9.986 74
|
Average edge multiplicity | m̃ = | 2.481 06
|
Size of LCC | N = | 449,042
|
Size of LSCC | Ns = | 22,664
|
Relative size of LSCC | Nrs = | 0.049 591 2
|
Diameter | δ = | 8
|
50-Percentile effective diameter | δ0.5 = | 3.002 60
|
90-Percentile effective diameter | δ0.9 = | 3.807 86
|
Median distance | δM = | 4
|
Mean distance | δm = | 3.286 39
|
Gini coefficient | G = | 0.865 655
|
Balanced inequality ratio | P = | 0.134 060
|
Outdegree balanced inequality ratio | P+ = | 0.078 420 1
|
Indegree balanced inequality ratio | P− = | 0.208 729
|
Relative edge distribution entropy | Her = | 0.703 860
|
Power law exponent | γ = | 3.649 44
|
Tail power law exponent | γt = | 1.851 00
|
Degree assortativity | ρ = | −0.384 446
|
Degree assortativity p-value | pρ = | 0.000 00
|
Clustering coefficient | c = | 0.000 217 896
|
Spectral norm | α = | 16,905.3
|
Operator 2-norm | ν = | 8,527.66
|
Cyclic eigenvalue | π = | 8,374.71
|
Algebraic connectivity | a = | 0.087 733 7
|
Spectral separation | |λ1[A] / λ2[A]| = | 1.606 98
|
Reciprocity | y = | 0.116 706
|
Non-bipartivity | bA = | 0.926 913
|
Normalized non-bipartivity | bN = | 0.030 047 7
|
Algebraic non-bipartivity | χ = | 0.087 219 0
|
Spectral bipartite frustration | bK = | 0.005 593 42
|
Controllability | C = | 420,007
|
Relative controllability | Cr = | 0.919 018
|
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.
|