Enron
The Enron email network consists of 1,148,072 emails sent between employees of
Enron between 1999 and 2003. Nodes in the network are individual employees and
edges are individual emails. It is possible to send an email to oneself, and
thus this network contains loops.
Metadata
Statistics
Size  n =  87,273

Volume  m =  1,148,072

Unique edge count  m̿ =  321,918

Wedge count  s =  49,424,399

Claw count  z =  13,894,959,120

Cross count  x =  3,805,295,597,320

Triangle count  t =  1,180,387

Square count  q =  92,985,807

4Tour count  T_{4} =  942,178,964

Maximum degree  d_{max} =  38,785

Maximum outdegree  d^{+}_{max} =  32,619

Maximum indegree  d^{−}_{max} =  6,166

Average degree  d =  26.309 9

Fill  p =  4.226 54 × 10^{−5}

Average edge multiplicity  m̃ =  3.566 35

Size of LCC  N =  84,384

Size of LSCC  N_{s} =  9,164

Relative size of LSCC  N^{r}_{s} =  0.105 004

Diameter  δ =  14

50Percentile effective diameter  δ_{0.5} =  4.405 46

90Percentile effective diameter  δ_{0.9} =  5.790 34

Mean distance  δ_{m} =  4.903 26

Gini coefficient  G =  0.907 511

Relative edge distribution entropy  H_{er} =  0.827 530

Power law exponent  γ =  2.651 60

Tail power law exponent  γ_{t} =  1.761 00

Degree assortativity  ρ =  −0.167 768

Degree assortativity pvalue  p_{ρ} =  0.000 00

In/outdegree correlation  ρ^{±} =  +0.403 110

Clustering coefficient  c =  0.071 648 0

Spectral norm  α =  7,808.15

Operator 2norm  ν =  3,904.14

Cyclic eigenvalue  π =  3,904.00

Algebraic connectivity  a =  0.004 157 70

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.702 73

Reciprocity  y =  0.146 497

Nonbipartivity  b_{A} =  0.622 292

Normalized nonbipartivity  b_{N} =  0.001 262 22

Spectral bipartite frustration  b_{K} =  8.928 20 × 10^{−5}

Controllability  C =  77,596

Relative controllability  C_{r} =  0.889 118

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]

Bryan Klimt and Yiming Yang.
The Enron corpus: A new dataset for email classification research.
In Proc. Eur. Conf. on Mach. Learn., pages 217–226, 2004.
