CiteULike tag–item

This is the bipartite network of tags and publications in CiteULike. Each edge represents a tag assignment that connects a tag and a publication. Since the same tag can be assigned to a single publication by multiple users, the network contains multiple edges. The edges are annotated with the creation time of the tag assignment.


Internal nameciteulike-ti
NameCiteULike tag–item
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Feature network
Dataset timestamp 2007
Node meaningTag, publication
Edge meaningAssignment
Network formatBipartite, undirected
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps


Size n =885,046
Left size n1 =153,277
Right size n2 =731,769
Volume m =2,411,819
Unique edge count m̿ =2,338,554
Wedge count s =21,880,206,884
Claw count z =1,213,193,044,873,518
Cross count x =5.511 04 × 1019
Square count q =383,860,055
4-Tour count T4 =90,596,413,384
Maximum degree dmax =189,295
Maximum left degree d1max =189,295
Maximum right degree d2max =1,646
Average degree d =5.450 16
Average left degree d1 =15.735 0
Average right degree d2 =3.295 87
Fill p =2.084 95 × 10−5
Average edge multiplicity m̃ =1.031 33
Size of LCC N =857,460
Diameter δ =24
50-Percentile effective diameter δ0.5 =3.912 25
90-Percentile effective diameter δ0.9 =5.970 78
Mean distance δm =4.768 26
Gini coefficient G =0.707 021
Balanced inequality ratio P =0.226 626
Left balanced inequality ratio P1 =0.126 018
Right balanced inequality ratio P2 =0.303 091
Relative edge distribution entropy Her =0.847 063
Power law exponent γ =2.325 35
Tail power law exponent γt =2.331 00
Degree assortativity ρ =−0.083 370 6
Degree assortativity p-value pρ =0.000 00
Spectral norm α =436.053
Algebraic connectivity a =0.001 147 49
Spectral separation 1[A] / λ2[A]| =1.430 65
Controllability C =645,431


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

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] Kevin Emamy and Richard Cameron. CiteULike: A researcher's social bookmarking service. Ariadne, (51), 2007.