Wikipedia talk (lv)

This is the communication network of the Latvian 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_lv
NameWikipedia talk (lv)
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 =41,424
Volume m =73,900
Unique edge count m̿ =51,098
Loop count l =8,740
Wedge count s =649,358,980
Claw count z =7,621,516,311,302
Cross count x =68,032,007,206,512,256
Triangle count t =10,408
Square count q =5,154,202
4-Tour count T4 =2,638,768,688
Maximum degree dmax =35,755
Maximum outdegree d+max =35,733
Maximum indegree dmax =2,220
Average degree d =3.567 98
Fill p =2.977 83 × 10−5
Average edge multiplicity m̃ =1.446 24
Size of LCC N =41,278
Size of LSCC Ns =510
Relative size of LSCC Nrs =0.012 311 7
Diameter δ =6
50-Percentile effective diameter δ0.5 =1.662 40
90-Percentile effective diameter δ0.9 =3.095 04
Median distance δM =2
Mean distance δm =2.355 89
Gini coefficient G =0.709 842
Relative edge distribution entropy Her =0.630 515
Power law exponent γ =9.150 63
Tail power law exponent γt =3.291 00
Degree assortativity ρ =−0.593 242
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.604 761
Clustering coefficient c =4.808 43 × 10−5
Spectral norm α =1,931.81
Operator 2-norm ν =981.703
Cyclic eigenvalue π =943.056
Algebraic connectivity a =0.080 124 8
Reciprocity y =0.040 451 7
Non-bipartivity bA =0.902 004
Spectral bipartite frustration bK =0.002 753 43


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

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.