Wikipedia talk (vi)
This is the communication network of the Vietnamese 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 = | 338,714
|
Volume | m = | 607,087
|
Unique edge count | m̿ = | 426,086
|
Loop count | l = | 43,802
|
Wedge count | s = | 6,861,145,804
|
Claw count | z = | 124,841,223,053,737
|
Cross count | x = | 2,025,346,130,080,254,208
|
Triangle count | t = | 138,848
|
Square count | q = | 279,874,289
|
4-Tour count | T4 = | 29,684,403,888
|
Maximum degree | dmax = | 78,340
|
Maximum outdegree | d+max = | 78,334
|
Maximum indegree | d−max = | 8,085
|
Average degree | d = | 3.584 66
|
Fill | p = | 3.713 91 × 10−6
|
Average edge multiplicity | m̃ = | 1.424 80
|
Size of LCC | N = | 337,273
|
Size of LSCC | Ns = | 2,785
|
Relative size of LSCC | Nrs = | 0.008 222 28
|
Diameter | δ = | 8
|
50-Percentile effective diameter | δ0.5 = | 3.392 56
|
90-Percentile effective diameter | δ0.9 = | 4.104 07
|
Median distance | δM = | 4
|
Mean distance | δm = | 3.731 68
|
Balanced inequality ratio | P = | 0.217 593
|
Outdegree balanced inequality ratio | P+ = | 0.042 310 2
|
Indegree balanced inequality ratio | P− = | 0.356 992
|
Relative edge distribution entropy | Her = | 0.677 591
|
Power law exponent | γ = | 9.350 86
|
Degree assortativity | ρ = | −0.437 310
|
Degree assortativity p-value | pρ = | 0.000 00
|
Clustering coefficient | c = | 6.071 06 × 10−5
|
Directed clustering coefficient | c± = | 0.030 005 7
|
Spectral norm | α = | 7,377.97
|
Operator 2-norm | ν = | 3,917.74
|
Cyclic eigenvalue | π = | 3,552.12
|
Algebraic connectivity | a = | 0.022 289 2
|
Spectral separation | |λ1[A] / λ2[A]| = | 2.170 98
|
Reciprocity | y = | 0.046 903 7
|
Non-bipartivity | bA = | 0.551 289
|
Normalized non-bipartivity | bN = | 0.006 308 98
|
Algebraic non-bipartivity | χ = | 0.014 534 6
|
Spectral bipartite frustration | bK = | 0.001 467 47
|
Controllability | C = | 332,293
|
Relative controllability | Cr = | 0.981 043
|
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.
|