Wikipedia talk (sv)
This is the communication network of the Swedish 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 = | 120,833
|
Volume | m = | 598,066
|
Unique edge count | m̿ = | 261,494
|
Loop count | l = | 145,000
|
Wedge count | s = | 1,716,076,564
|
Claw count | z = | 19,545,287,753,562
|
Cross count | x = | 182,630,394,452,724,064
|
Triangle count | t = | 401,802
|
Square count | q = | 780,631,206
|
4-Tour count | T4 = | 13,109,831,006
|
Maximum degree | dmax = | 77,916
|
Maximum outdegree | d+max = | 77,915
|
Maximum indegree | d−max = | 6,261
|
Average degree | d = | 9.899 05
|
Fill | p = | 1.790 98 × 10−5
|
Average edge multiplicity | m̃ = | 2.287 11
|
Size of LCC | N = | 119,327
|
Size of LSCC | Ns = | 6,752
|
Relative size of LSCC | Nrs = | 0.055 878 8
|
Diameter | δ = | 8
|
50-Percentile effective diameter | δ0.5 = | 2.603 98
|
90-Percentile effective diameter | δ0.9 = | 3.629 07
|
Median distance | δM = | 3
|
Mean distance | δm = | 3.123 74
|
Gini coefficient | G = | 0.835 312
|
Balanced inequality ratio | P = | 0.159 254
|
Outdegree balanced inequality ratio | P+ = | 0.075 618 7
|
Indegree balanced inequality ratio | P− = | 0.246 327
|
Relative edge distribution entropy | Her = | 0.730 291
|
Power law exponent | γ = | 3.039 23
|
Tail power law exponent | γt = | 1.721 00
|
Tail power law exponent with p | γ3 = | 1.721 00
|
p-value | p = | 0.004 000 00
|
Outdegree tail power law exponent with p | γ3,o = | 1.601 00
|
Outdegree p-value | po = | 0.002 000 00
|
Indegree tail power law exponent with p | γ3,i = | 2.081 00
|
Indegree p-value | pi = | 0.000 00
|
Degree assortativity | ρ = | −0.268 996
|
Degree assortativity p-value | pρ = | 0.000 00
|
In/outdegree correlation | ρ± = | +0.673 375
|
Clustering coefficient | c = | 0.000 702 420
|
Directed clustering coefficient | c± = | 0.022 286 4
|
Spectral norm | α = | 5,777.92
|
Cyclic eigenvalue | π = | 2,747.71
|
Algebraic connectivity | a = | 0.146 200
|
Non-bipartivity | bA = | 0.827 077
|
Normalized non-bipartivity | bN = | 0.042 919 5
|
Algebraic non-bipartivity | χ = | 0.166 034
|
Spectral bipartite frustration | bK = | 0.009 971 48
|
Controllability | C = | 107,592
|
Relative controllability | Cr = | 0.890 419
|
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.
|