YouTube
This is the bipartite network of YouTube users and their group memberships. The
nodes are users and groups, and an edge between a user and a group denotes a
group membership.
Metadata
Statistics
Size  n =  124,325

Left size  n_{1} =  94,238

Right size  n_{2} =  30,087

Volume  m =  293,360

Wedge count  s =  70,180,198

Claw count  z =  92,191,098,295

Cross count  x =  150,344,737,942,342

Square count  q =  12,540,261

4Tour count  T_{4} =  381,648,116

Maximum degree  d_{max} =  7,591

Maximum left degree  d_{1max} =  1,035

Maximum right degree  d_{2max} =  7,591

Average degree  d =  4.719 24

Average left degree  d_{1} =  3.112 97

Average right degree  d_{2} =  9.750 39

Fill  p =  0.000 103 466

Size of LCC  N =  113,496

Diameter  δ =  17

50Percentile effective diameter  δ_{0.5} =  4.602 49

90Percentile effective diameter  δ_{0.9} =  6.390 19

Median distance  δ_{M} =  5

Mean distance  δ_{m} =  5.174 35

Gini coefficient  G =  0.693 488

Balanced inequality ratio  P =  0.229 794

Left balanced inequality ratio  P_{1} =  0.280 584

Right balanced inequality ratio  P_{2} =  0.188 134

Relative edge distribution entropy  H_{er} =  0.878 122

Power law exponent  γ =  2.411 68

Tail power law exponent  γ_{t} =  2.311 00

Tail power law exponent with p  γ_{3} =  2.311 00

pvalue  p =  0.028 000 0

Left tail power law exponent with p  γ_{3,1} =  2.801 00

Left pvalue  p_{1} =  0.772 000

Right tail power law exponent with p  γ_{3,2} =  2.281 00

Right pvalue  p_{2} =  0.278 000

Degree assortativity  ρ =  −0.067 164 5

Degree assortativity pvalue  p_{ρ} =  2.122 07 × 10^{−290}

Spectral norm  α =  90.377 5

Algebraic connectivity  a =  0.009 221 19

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.235 41

Controllability  C =  73,201

Relative controllability  C_{r} =  0.588 787

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]

Alan Mislove.
Online Social Networks: Measurement, Analysis, and Applications
to Distributed Information Systems.
PhD thesis, Rice Univ., 2009.
