Wikipedia talk (ja)
This is the communication network of the Japanese 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 = | 397,635
|
Volume | m = | 1,031,378
|
Unique edge count | m̿ = | 656,330
|
Loop count | l = | 193,900
|
Wedge count | s = | 18,963,253,905
|
Claw count | z = | 891,394,544,223,911
|
Triangle count | t = | 251,868
|
Square count | q = | 1,272,886,511
|
4-Tour count | T4 = | 86,037,333,232
|
Maximum degree | dmax = | 170,852
|
Maximum outdegree | d+max = | 170,851
|
Maximum indegree | d−max = | 3,633
|
Average degree | d = | 5.187 56
|
Average edge multiplicity | m̃ = | 1.571 43
|
Size of LCC | N = | 394,528
|
Size of LSCC | Ns = | 14,477
|
Relative size of LSCC | Nrs = | 0.036 407 8
|
Diameter | δ = | 10
|
50-Percentile effective diameter | δ0.5 = | 3.188 88
|
90-Percentile effective diameter | δ0.9 = | 3.862 74
|
Median distance | δM = | 4
|
Mean distance | δm = | 3.380 54
|
Balanced inequality ratio | P = | 0.182 588
|
Outdegree balanced inequality ratio | P+ = | 0.100 564
|
Indegree balanced inequality ratio | P− = | 0.276 959
|
Power law exponent | γ = | 4.517 50
|
Tail power law exponent | γt = | 1.921 00
|
Degree assortativity | ρ = | −0.281 185
|
Degree assortativity p-value | pρ = | 0.000 00
|
In/outdegree correlation | ρ± = | +0.715 813
|
Clustering coefficient | c = | 3.984 57 × 10−5
|
Directed clustering coefficient | c± = | 0.010 871 6
|
Spectral norm | α = | 2,914.80
|
Operator 2-norm | ν = | 1,464.24
|
Cyclic eigenvalue | π = | 1,449.41
|
Algebraic connectivity | a = | 0.046 582 3
|
Spectral separation | |λ1[A] / λ2[A]| = | 1.462 55
|
Reciprocity | y = | 0.094 636 8
|
Non-bipartivity | bA = | 0.827 669
|
Normalized non-bipartivity | bN = | 0.027 208 6
|
Algebraic non-bipartivity | χ = | 0.053 922 5
|
Spectral bipartite frustration | bK = | 0.004 189 78
|
Controllability | C = | 369,235
|
Relative controllability | Cr = | 0.928 578
|
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.
|