Twitter tag–item

This is a bipartite network of tag–URL relations in Twitter. Left nodes represent tags and right nodes represent URLs. An edge shows that an URL was mentioned together with a given tag in a tweet.


Internal namemunmun_twitterex_ti
NameTwitter tag–item
Data source
AvailabilityDataset is not available for download
Consistency checkDataset passed all tests
Feature network
Dataset timestamp 2010
Node meaningHashtag, URL
Edge meaningCo-occurrence
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 =1,502,611
Left size n1 =270,582
Right size n2 =1,232,029
Volume m =2,635,885
Unique edge count m̿ =1,996,158
Wedge count s =4,418,323,415
Claw count z =51,877,537,933,158
Square count q =70,327,726
4-Tour count T4 =18,239,948,076
Maximum degree dmax =84,158
Maximum left degree d1max =70,377
Maximum right degree d2max =84,158
Average degree d =3.508 41
Average left degree d1 =9.741 54
Average right degree d2 =2.139 47
Fill p =5.987 91 × 10−6
Average edge multiplicity m̃ =1.320 48
Size of LCC N =1,234,934
Diameter δ =31
50-Percentile effective diameter δ0.5 =5.383 58
90-Percentile effective diameter δ0.9 =7.672 44
Median distance δM =6
Mean distance δm =5.911 57
Gini coefficient G =0.671 119
Balanced inequality ratio P =0.244 057
Left balanced inequality ratio P1 =0.133 490
Right balanced inequality ratio P2 =0.335 298
Power law exponent γ =3.792 46
Tail power law exponent γt =1.941 00
Degree assortativity ρ =−0.036 948 5
Degree assortativity p-value pρ =0.000 00
Spectral norm α =23,853.3
Algebraic connectivity a =0.000 301 907
Spectral separation 1[A] / λ2[A]| =2.926 07
Controllability C =1,040,405
Relative controllability Cr =0.692 398


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

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] 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.