Facebook (NIPS)
This directed networks contains Facebook user–user friendships. A node
represents a user. An edge indicates that the user represented by the left node
is a friend of the user represented by the right node.
Metadata
Statistics
Size  n =  2,888

Volume  m =  2,981

Loop count  l =  0

Wedge count  s =  759,641

Claw count  z =  160,449,784

Cross count  x =  27,585,555,395

Triangle count  t =  91

Square count  q =  1,261

4Tour count  T_{4} =  3,054,614

Maximum degree  d_{max} =  769

Average degree  d =  2.064 40

Fill  p =  0.000 715 069

Size of LCC  N =  2,888

Diameter  δ =  9

50Percentile effective diameter  δ_{0.5} =  3.420 92

90Percentile effective diameter  δ_{0.9} =  5.524 62

Median distance  δ_{M} =  4

Mean distance  δ_{m} =  3.980 79

Gini coefficient  G =  0.514 083

Relative edge distribution entropy  H_{er} =  0.708 747

Power law exponent  γ =  25.589 3

Tail power law exponent  γ_{t} =  4.521 00

Tail power law exponent with p  γ_{3} =  4.521 00

pvalue  p =  0.000 00

Degree assortativity  ρ =  −0.668 214

Degree assortativity pvalue  p_{ρ} =  0.000 00

Clustering coefficient  c =  0.000 359 380

Spectral norm  α =  27.803 1

Algebraic connectivity  a =  0.002 377 13

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.010 22

Nonbipartivity  b_{A} =  0.002 518 41

Normalized nonbipartivity  b_{N} =  0.001 554 35

Algebraic nonbipartivity  χ =  0.003 114 08

Spectral bipartite frustration  b_{K} =  0.000 377 116

Controllability  C =  3,989

Relative controllability  C_{r} =  0.997 499

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.
