Wikipedia talk (bn)

This is the communication network of the Bangla 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_bn
NameWikipedia talk (bn)
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 =83,803
Volume m =122,078
Unique edge count m̿ =99,789
Loop count l =7,810
Wedge count s =607,190,714
Claw count z =3,752,050,222,613
Cross count x =19,040,630,407,127,920
Triangle count t =14,552
Square count q =7,409,430
4-Tour count T4 =2,488,232,542
Maximum degree dmax =21,778
Maximum outdegree d+max =21,746
Maximum indegree dmax =1,440
Average degree d =2.913 45
Fill p =1.420 90 × 10−5
Average edge multiplicity m̃ =1.223 36
Size of LCC N =83,521
Size of LSCC Ns =700
Relative size of LSCC Nrs =0.008 352 92
Diameter δ =7
50-Percentile effective diameter δ0.5 =3.286 95
90-Percentile effective diameter δ0.9 =4.085 39
Median distance δM =4
Mean distance δm =3.617 18
Gini coefficient G =0.650 115
Balanced inequality ratio P =0.253 993
Outdegree balanced inequality ratio P+ =0.046 879 9
Indegree balanced inequality ratio P =0.404 651
Relative edge distribution entropy Her =0.676 965
Power law exponent γ =11.294 7
Tail power law exponent γt =3.531 00
Degree assortativity ρ =−0.510 017
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =7.189 83 × 10−5
Directed clustering coefficient c± =0.011 875 9
Spectral norm α =1,262.02
Operator 2-norm ν =640.447
Cyclic eigenvalue π =616.099
Algebraic connectivity a =0.018 549 5
Spectral separation 1[A] / λ2[A]| =1.531 08
Reciprocity y =0.037 529 2
Non-bipartivity bA =0.795 504
Normalized non-bipartivity bN =0.011 150 6
Algebraic non-bipartivity χ =0.024 876 4
Spectral bipartite frustration bK =0.002 638 66
Controllability C =82,034
Relative controllability Cr =0.978 891


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.