DNC emails
This is the directed network of emails in the 2016 Democratic National
Committee email leak. The Democratic National Committee (DNC) is the formal
governing body for the United States Democratic Party. A dump of emails of the
DNC was leaked in 2016. Nodes in the network correspond to persons in the
dataset. A directed edge in the dataset denotes that a person has sent an
email to another person. Since an email can have any number of recipients, a
single email is mapped to multiple edges in this dataset, resulting in the
number of edges in this network being about twice the number of emails in the
dump.
Metadata
Statistics
Size  n =  2,029

Volume  m =  39,264

Unique edge count  m̿ =  5,598

Wedge count  s =  317,905

Claw count  z =  59,899,010

Cross count  x =  6,944,032,926

Triangle count  t =  9,431

Square count  q =  209,206

4Tour count  T_{4} =  2,954,036

Maximum degree  d_{max} =  5,813

Maximum outdegree  d^{+}_{max} =  4,073

Maximum indegree  d^{−}_{max} =  2,951

Average degree  d =  38.702 8

Fill  p =  0.001 565 49

Average edge multiplicity  m̃ =  7.013 93

Size of LCC  N =  1,833

Size of LSCC  N_{s} =  520

Relative size of LSCC  N^{r}_{s} =  0.256 284

Diameter  δ =  8

50Percentile effective diameter  δ_{0.5} =  2.824 58

90Percentile effective diameter  δ_{0.9} =  3.982 21

Median distance  δ_{M} =  3

Mean distance  δ_{m} =  3.378 96

Gini coefficient  G =  0.911 291

Balanced inequality ratio  P =  0.096 653 4

Outdegree balanced inequality ratio  P_{+} =  0.104 498

Indegree balanced inequality ratio  P_{−} =  0.112 419

Relative edge distribution entropy  H_{er} =  0.790 349

Power law exponent  γ =  2.773 64

Tail power law exponent  γ_{t} =  2.011 00

Tail power law exponent with p  γ_{3} =  2.011 00

pvalue  p =  0.000 00

Outdegree tail power law exponent with p  γ_{3,o} =  1.951 00

Outdegree pvalue  p_{o} =  0.000 00

Indegree tail power law exponent with p  γ_{3,i} =  2.011 00

Indegree pvalue  p_{i} =  0.002 000 00

Degree assortativity  ρ =  −0.306 550

Degree assortativity pvalue  p_{ρ} =  3.924 42 × 10^{−190}

Clustering coefficient  c =  0.088 998 3

Spectral norm  α =  1,566.51

Algebraic connectivity  a =  0.047 944 7

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.073 48

Reciprocity  y =  0.419 257

Controllability  C =  1,500

Relative controllability  C_{r} =  0.793 231

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 ]
