Twitter lists
This directed networks contains Twitter user–user following information. A
node represents a user. An edge indicates that the user represented by the left
node follows the user represented by the right node.
Metadata
Statistics
Size  n =  23,370

Volume  m =  33,101

Loop count  l =  0

Wedge count  s =  1,231,177

Claw count  z =  43,439,459

Cross count  x =  1,457,090,152

Triangle count  t =  8,804

Square count  q =  165,762

4Tour count  T_{4} =  6,316,466

Maximum degree  d_{max} =  239

Maximum outdegree  d^{+}_{max} =  238

Maximum indegree  d^{−}_{max} =  57

Average degree  d =  2.832 78

Fill  p =  6.060 97 × 10^{−5}

Size of LCC  N =  22,322

Size of LSCC  N_{s} =  38

Relative size of LSCC  N^{r}_{s} =  0.001 626 02

Diameter  δ =  15

50Percentile effective diameter  δ_{0.5} =  5.636 00

90Percentile effective diameter  δ_{0.9} =  7.559 40

Median distance  δ_{M} =  6

Mean distance  δ_{m} =  6.188 24

Gini coefficient  G =  0.613 256

Balanced inequality ratio  P =  0.259 448

Outdegree balanced inequality ratio  P_{+} =  0.311 803

Indegree balanced inequality ratio  P_{−} =  0.409 293

Relative edge distribution entropy  H_{er} =  0.867 531

Power law exponent  γ =  4.333 57

Tail power law exponent  γ_{t} =  2.471 00

Tail power law exponent with p  γ_{3} =  2.471 00

pvalue  p =  0.000 00

Outdegree tail power law exponent with p  γ_{3,o} =  2.941 00

Outdegree pvalue  p_{o} =  0.000 00

Indegree tail power law exponent with p  γ_{3,i} =  2.841 00

Indegree pvalue  p_{i} =  0.000 00

Degree assortativity  ρ =  −0.477 982

Degree assortativity pvalue  p_{ρ} =  0.000 00

In/outdegree correlation  ρ^{±} =  −0.058 215 9

Clustering coefficient  c =  0.021 452 6

Directed clustering coefficient  c^{±} =  0.207 821

Spectral norm  α =  25.209 8

Operator 2norm  ν =  20.574 3

Cyclic eigenvalue  π =  6.316 63

Algebraic connectivity  a =  0.005 031 58

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.051 94

Reciprocity  y =  0.016 313 7

Nonbipartivity  b_{A} =  0.335 034

Normalized nonbipartivity  b_{N} =  0.002 550 68

Algebraic nonbipartivity  χ =  0.005 056 39

Spectral bipartite frustration  b_{K} =  0.000 443 346

Controllability  C =  22,432

Relative controllability  C_{r} =  0.959 863

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]

Julian McAuley and Jure Leskovec.
Learning to discover social circles in ego networks.
In Adv. in Neural Inf. Process. Syst., pages 548–556. 2012.
