Reality Mining
This undirected network contains human contact data among 100 students of the
Massachusetts Institute of Technology (MIT), collected by the Reality Mining
experiment performed in 2004 as part of the Reality Commons project. The data
was collected over 9 months using 100 mobile phones. A node represents a
person; an edge indicates that the corresponding nodes had physical contact.
Metadata
Statistics
Size  n =  96

Volume  m =  1,086,404

Unique edge count  m̿ =  2,539

Loop count  l =  0

Wedge count  s =  149,335

Claw count  z =  3,073,975

Cross count  x =  48,933,259

Triangle count  t =  36,108

Square count  q =  1,534,878

4Tour count  T_{4} =  12,881,442

Maximum degree  d_{max} =  98,257

Average degree  d =  22,633.4

Fill  p =  0.556 798

Average edge multiplicity  m̃ =  427.887

Size of LCC  N =  96

Diameter  δ =  3

50Percentile effective diameter  δ_{0.5} =  0.795 737

90Percentile effective diameter  δ_{0.9} =  1.733 01

Median distance  δ_{M} =  1

Mean distance  δ_{m} =  1.363 27

Gini coefficient  G =  0.488 433

Balanced inequality ratio  P =  0.326 958

Relative edge distribution entropy  H_{er} =  0.982 596

Power law exponent  γ =  1.317 13

Tail power law exponent  γ_{t} =  2.611 00

Tail power law exponent with p  γ_{3} =  2.611 00

pvalue  p =  0.000 00

Degree assortativity  ρ =  −0.055 486 2

Degree assortativity pvalue  p_{ρ} =  7.620 70 × 10^{−5}

Clustering coefficient  c =  0.725 376

Spectral norm  α =  61,801.9

Algebraic connectivity  a =  4.041 03

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.950 75

Nonbipartivity  b_{A} =  0.563 644

Normalized nonbipartivity  b_{N} =  0.783 795

Spectral bipartite frustration  b_{K} =  0.009 327 28

Controllability  C =  1

Relative controllability  C_{r} =  0.010 416 7

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]

Nathan Eagle and Alex (Sandy) Pentland.
Reality Mining: Sensing complex social systems.
Personal Ubiquitous Comput., 10(4):255–268, 2006.
