Wikipedia talk (pl)

This is the communication network of the Polish 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_pl
NameWikipedia talk (pl)
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 =155,820
Volume m =1,358,426
Unique edge count m̿ =549,603
Wedge count s =3,473,856,834
Triangle count t =2,292,943
Square count q =1,928,005,260
4-Tour count T4 =29,320,369,940
Maximum degree dmax =112,031
Maximum outdegree d+max =112,031
Maximum indegree dmax =8,726
Average degree d =17.435 8
Average edge multiplicity m̃ =2.471 65
Size of LCC N =153,167
Diameter δ =7
50-Percentile effective diameter δ0.5 =2.621 82
90-Percentile effective diameter δ0.9 =3.671 64
Median distance δM =3
Mean distance δm =3.127 80
Power law exponent γ =2.433 03
Degree assortativity ρ =−0.189 731
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.001 980 17
Operator 2-norm ν =6,183.55
Algebraic connectivity a =0.110 017
Algebraic non-bipartivity χ =0.106 509
Spectral bipartite frustration bK =0.004 392 51


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

Average neighbor degree distribution

Temporal distribution

Temporal hop 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.