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.


Internal namewiki_talk_ja
NameWikipedia talk (ja)
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Communication network
Dataset timestamp 2017-10-27
Node meaningUser
Edge meaningMessage
Network formatUnipartite, directed
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsContains loops


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
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 dmax =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
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
Algebraic connectivity a =0.046 582 3
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


Fruchterman–Reingold graph drawing

Degree distribution

Cumulative degree distribution

Lorenz curve

Spectral distribution of the adjacency matrix

Spectral distribution of the normalized adjacency matrix

Spectral distribution of the Laplacian

Spectral graph drawing based on the adjacency matrix

Spectral graph drawing based on the Laplacian

Spectral graph drawing based on the normalized adjacency matrix

Degree assortativity

Zipf plot

Hop distribution

Delaunay graph drawing

In/outdegree scatter plot

Edge weight/multiplicity distribution

Clustering coefficient distribution

Temporal distribution

Diameter/density evolution


Matrix decompositions plots



[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.