Twitter user–tag

This is a bipartite network consisting of Twitter users and tags they mentioned in their postings. Left nodes represent users and right nodes represent tags. An edge shows that a tag was used by a user in a tweet.


Internal namemunmun_twitterex_ut
NameTwitter user–tag
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Interaction network
Dataset timestamp 2010
Node meaningUser, hashtag
Edge meaningUsage
Network formatBipartite, undirected
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps
Snapshot Is a snapshot and likely to not contain all data


Size n =705,632
Left size n1 =175,214
Right size n2 =530,418
Volume m =4,664,605
Unique edge count m̿ =1,890,661
Wedge count s =1,006,768,611
Claw count z =3,620,991,361,242
Cross count x =14,011,028,889,674,202
Square count q =206,508,691
4-Tour count T4 =5,682,999,878
Maximum degree dmax =90,362
Maximum left degree d1max =2,431
Maximum right degree d2max =90,362
Average degree d =13.221 1
Average left degree d1 =26.622 3
Average right degree d2 =8.794 21
Fill p =2.034 35 × 10−5
Average edge multiplicity m̃ =2.467 18
Size of LCC N =690,906
Diameter δ =16
50-Percentile effective diameter δ0.5 =5.022 02
90-Percentile effective diameter δ0.9 =5.893 74
Mean distance δm =5.319 70
Gini coefficient G =0.842 861
Balanced inequality ratio P =0.149 222
Left balanced inequality ratio P1 =0.236 392
Right balanced inequality ratio P2 =0.135 109
Relative edge distribution entropy Her =0.882 745
Power law exponent γ =2.460 48
Tail power law exponent γt =2.601 00
Degree assortativity ρ =−0.098 655 9
Degree assortativity p-value pρ =0.000 00
Spectral norm α =2,216.48
Algebraic connectivity a =0.003 077 89


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] Munmun De Choudhury, Yu-Ru Lin, Hari Sundaram, K. Selçuk Candan, Lexing Xie, and Aisling Kelliher. How does the data sampling strategy impact the discovery of information diffusion in social media? In ICWSM, pages 34–41, 2010.