Wikipedia talk (eo)

This is the communication network of the Esperanto 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_eo
NameWikipedia talk (eo)
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 =7,586
Volume m =47,070
Unique edge count m̿ =17,362
Loop count l =9,827
Wedge count s =6,334,328
Claw count z =3,412,850,433
Cross count x =1,415,995,053,128
Triangle count t =18,044
Square count q =2,062,111
4-Tour count T4 =41,862,732
Maximum degree dmax =5,157
Maximum outdegree d+max =3,540
Maximum indegree dmax =2,380
Average degree d =12.409 7
Fill p =0.000 301 699
Average edge multiplicity m̃ =2.711 09
Size of LCC N =7,253
Size of LSCC Ns =822
Relative size of LSCC Nrs =0.108 358
Diameter δ =8
50-Percentile effective diameter δ0.5 =2.575 43
90-Percentile effective diameter δ0.9 =3.630 51
Median distance δM =3
Mean distance δm =3.095 16
Gini coefficient G =0.865 440
Balanced inequality ratio P =0.132 813
Outdegree balanced inequality ratio P+ =0.102 549
Indegree balanced inequality ratio P =0.194 370
Relative edge distribution entropy Her =0.758 358
Power law exponent γ =3.047 78
Tail power law exponent γt =2.031 00
Tail power law exponent with p γ3 =2.031 00
p-value p =0.026 000 0
Outdegree tail power law exponent with p γ3,o =2.011 00
Outdegree p-value po =0.000 00
Indegree tail power law exponent with p γ3,i =2.211 00
Indegree p-value pi =0.196 000
Degree assortativity ρ =−0.423 923
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.715 506
Clustering coefficient c =0.008 545 82
Directed clustering coefficient c± =0.056 901 0
Spectral norm α =1,762.80
Operator 2-norm ν =889.457
Cyclic eigenvalue π =871.028
Algebraic connectivity a =0.101 624
Spectral separation 1[A] / λ2[A]| =1.422 85
Reciprocity y =0.279 058
Non-bipartivity bA =0.753 769
Normalized non-bipartivity bN =0.056 411 3
Algebraic non-bipartivity χ =0.100 022
Spectral bipartite frustration bK =0.005 931 62
Controllability C =6,049
Relative controllability Cr =0.797 390


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

Double Laplacian graph drawing

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.