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.
Metadata
Statistics
Size  n =  705,632

Left size  n_{1} =  175,214

Right size  n_{2} =  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

4Tour count  T_{4} =  5,682,999,878

Maximum degree  d_{max} =  90,362

Maximum left degree  d_{1max} =  2,431

Maximum right degree  d_{2max} =  90,362

Average degree  d =  13.221 1

Average left degree  d_{1} =  26.622 3

Average right degree  d_{2} =  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

50Percentile effective diameter  δ_{0.5} =  5.022 02

90Percentile effective diameter  δ_{0.9} =  5.893 74

Median distance  δ_{M} =  6

Mean distance  δ_{m} =  5.319 70

Gini coefficient  G =  0.842 861

Balanced inequality ratio  P =  0.149 222

Left balanced inequality ratio  P_{1} =  0.236 392

Right balanced inequality ratio  P_{2} =  0.135 109

Relative edge distribution entropy  H_{er} =  0.882 745

Power law exponent  γ =  2.460 48

Tail power law exponent  γ_{t} =  2.601 00

Degree assortativity  ρ =  −0.098 655 9

Degree assortativity pvalue  p_{ρ} =  0.000 00

Spectral norm  α =  2,216.48

Algebraic connectivity  a =  0.003 077 89

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.010 44

Controllability  C =  437,932

Relative controllability  C_{r} =  0.620 624

Plots
Matrix decompositions plots
Downloads
References
[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, YuRu 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.
