Twitter mentions
This is the directed network of "@username" mentions on Twitter. Each node is a
Twitter user, and each directed edge from user A to user B means that user A
has mentioned user B in a tweet using the "@username" syntax. Multiple edges
are allowed, and each edge is annotated with the timestamp of the tweet. Since
it is possible to mention one's own username, this network contains loops.
Metadata
Statistics
Size  n =  2,919,613

Volume  m =  12,887,063

Unique edge count  m̿ =  7,301,101

Loop count  l =  235,300

Wedge count  s =  736,717,625

Claw count  z =  690,658,257,498

Cross count  x =  1,640,036,248,049,757

Triangle count  t =  1,449,797

Square count  q =  81,161,696

4Tour count  T_{4} =  3,610,479,352

Maximum degree  d_{max} =  39,753

Maximum outdegree  d^{+}_{max} =  1,697

Maximum indegree  d^{−}_{max} =  39,753

Average degree  d =  8.827 93

Fill  p =  8.565 21 × 10^{−7}

Average edge multiplicity  m̃ =  1.765 08

Size of LCC  N =  2,893,623

Size of LSCC  N_{s} =  98,784

Relative size of LSCC  N^{r}_{s} =  0.033 834 6

Diameter  δ =  23

50Percentile effective diameter  δ_{0.5} =  4.987 02

90Percentile effective diameter  δ_{0.9} =  5.908 45

Median distance  δ_{M} =  5

Mean distance  δ_{m} =  5.454 36

Gini coefficient  G =  0.798 378

Balanced inequality ratio  P =  0.164 585

Outdegree balanced inequality ratio  P_{+} =  0.281 465

Indegree balanced inequality ratio  P_{−} =  0.229 310

Relative edge distribution entropy  H_{er} =  0.894 306

Power law exponent  γ =  2.845 35

Tail power law exponent  γ_{t} =  1.861 00

Degree assortativity  ρ =  −0.010 567 6

Degree assortativity pvalue  p_{ρ} =  0.000 00

Clustering coefficient  c =  0.005 903 74

Directed clustering coefficient  c^{±} =  0.025 903 2

Spectral norm  α =  1,114.49

Operator 2norm  ν =  1,112.63

Cyclic eigenvalue  π =  345.070

Reciprocity  y =  0.031 959 8

Nonbipartivity  b_{A} =  0.002 874 42

Normalized nonbipartivity  b_{N} =  0.000 598 655

Algebraic nonbipartivity  χ =  0.001 197 18

Spectral bipartite frustration  b_{K} =  6.019 94 × 10^{−5}

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.
