BibSonomy user–tag

This is the bipartite user–tag network from BibSonomy. Each edge represents a tag assignment. The network allows multiple edge as users may use a tag for multiple documents. The network is complete as it is taken from the official BibSonomy dump.


Internal namebibsonomy-2ut
NameBibSonomy user–tag
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Interaction network
Dataset timestamp 2010
Node meaningUser, tag
Edge meaningAssignment
Network formatBipartite, undirected
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps


Size n =210,467
Left size n1 =5,794
Right size n2 =204,673
Volume m =2,555,080
Unique edge count m̿ =453,987
Wedge count s =991,843,705
Claw count z =4,596,638,282,952
Cross count x =19,818,609,712,155,388
Square count q =305,062,057
4-Tour count T4 =6,409,052,574
Maximum degree dmax =428,436
Maximum left degree d1max =428,436
Maximum right degree d2max =182,908
Average degree d =24.280 1
Average left degree d1 =440.987
Average right degree d2 =12.483 7
Fill p =0.000 382 829
Average edge multiplicity m̃ =5.628 09
Size of LCC N =209,357
Diameter δ =12
50-Percentile effective diameter δ0.5 =3.517 73
90-Percentile effective diameter δ0.9 =4.011 15
Median distance δM =4
Mean distance δm =4.051 36
Gini coefficient G =0.926 128
Balanced inequality ratio P =0.089 785 8
Left balanced inequality ratio P1 =0.080 483 2
Right balanced inequality ratio P2 =0.134 076
Relative edge distribution entropy Her =0.772 367
Power law exponent γ =3.674 91
Tail power law exponent γt =1.961 00
Tail power law exponent with p γ3 =1.961 00
p-value p =0.271 000
Left tail power law exponent with p γ3,1 =1.681 00
Left p-value p1 =0.000 00
Right tail power law exponent with p γ3,2 =2.201 00
Right p-value p2 =0.000 00
Degree assortativity ρ =−0.152 058
Degree assortativity p-value pρ =0.000 00
Spectral norm α =62,391.4
Algebraic connectivity a =0.016 601 9
Spectral separation 1[A] / λ2[A]| =1.105 36
Controllability C =199,969
Relative controllability Cr =0.950 120


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

Edge weight/multiplicity distribution

Temporal distribution

Diameter/density evolution

Inter-event distribution

Node-level inter-event 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] Dominik Benz, Andreas Hotho, Robert Jäschke, Beate Krause, Folke Mitzlaff, Christoph Schmitz, and Gerd Stumme. The social bookmark and publication management system BibSonomy. The VLDB J., 19(6):849–875, dec 2010.