UC Irvine messages
This directed network contains sent messages between the users of an online
community of students from the University of California, Irvine. A node
represents a user. A directed edge represents a sent message. Multiple edges
denote multiple messages.
Metadata
Statistics
Size  n =  1,899

Volume  m =  59,835

Unique edge count  m̿ =  20,296

Loop count  l =  0

Wedge count  s =  755,882

Claw count  z =  82,140,043

Cross count  x =  4,357,774,499

Triangle count  t =  14,319

Square count  q =  729,064

4Tour count  T_{4} =  8,883,716

Maximum degree  d_{max} =  1,546

Maximum outdegree  d^{+}_{max} =  1,091

Maximum indegree  d^{−}_{max} =  558

Average degree  d =  63.017 4

Fill  p =  0.005 631 05

Average edge multiplicity  m̃ =  2.948 12

Size of LCC  N =  1,893

Size of LSCC  N_{s} =  1,294

Relative size of LSCC  N^{r}_{s} =  0.681 411

Diameter  δ =  8

50Percentile effective diameter  δ_{0.5} =  2.557 66

90Percentile effective diameter  δ_{0.9} =  3.657 93

Median distance  δ_{M} =  3

Mean distance  δ_{m} =  3.068 97

Gini coefficient  G =  0.753 661

Relative edge distribution entropy  H_{er} =  0.889 659

Power law exponent  γ =  1.562 87

Tail power law exponent  γ_{t} =  2.831 00

Degree assortativity  ρ =  −0.187 776

Degree assortativity pvalue  p_{ρ} =  4.937 81 × 10^{−218}

In/outdegree correlation  ρ^{±} =  +0.900 204

Clustering coefficient  c =  0.056 830 3

Spectral norm  α =  384.619

Operator 2norm  ν =  229.350

Cyclic eigenvalue  π =  181.856

Algebraic connectivity  a =  0.349 550

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.149 58

Reciprocity  y =  0.636 382

Nonbipartivity  b_{A} =  0.130 119

Normalized nonbipartivity  b_{N} =  0.132 355

Spectral bipartite frustration  b_{K} =  0.003 357 53

Controllability  C =  634

Relative controllability  C_{r} =  0.333 860

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]

Tore Opsahl and Pietro Panzarasa.
Clustering in weighted networks.
Soc. Netw., 31(2):155–163, 2009.
