FOLDOC
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Computing (FOLDOC, www.foldoc.org). Nodes are entries, and a directed edge
from term A to term B denotes that the term B is used in the definition of term
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Metadata
Statistics
Size  n =  13,356

Volume  m =  125,207

Unique edge count  m̿ =  120,238

Loop count  l =  0

Wedge count  s =  2,762,280

Claw count  z =  199,487,042

Cross count  x =  22,160,884,702

Triangle count  t =  104,225

Square count  q =  739,721

4Tour count  T_{4} =  17,149,830

Maximum degree  d_{max} =  752

Maximum outdegree  d^{+}_{max} =  102

Maximum indegree  d^{−}_{max} =  720

Average degree  d =  18.749 2

Fill  p =  0.000 674 096

Average edge multiplicity  m̃ =  1.041 33

Size of LCC  N =  13,356

Size of LSCC  N_{s} =  13,274

Relative size of LSCC  N^{r}_{s} =  0.993 860

Diameter  δ =  8

50Percentile effective diameter  δ_{0.5} =  3.506 44

90Percentile effective diameter  δ_{0.9} =  4.581 42

Median distance  δ_{M} =  4

Mean distance  δ_{m} =  3.990 87

Gini coefficient  G =  0.254 314

Balanced inequality ratio  P =  0.413 068

Outdegree balanced inequality ratio  P_{+} =  0.423 666

Indegree balanced inequality ratio  P_{−} =  0.373 493

Relative edge distribution entropy  H_{er} =  0.983 667

Power law exponent  γ =  1.736 78

Tail power law exponent  γ_{t} =  2.891 00

Tail power law exponent with p  γ_{3} =  2.891 00

pvalue  p =  0.646 000

Outdegree tail power law exponent with p  γ_{3,o} =  5.461 00

Outdegree pvalue  p_{o} =  0.009 000 00

Indegree tail power law exponent with p  γ_{3,i} =  2.581 00

Indegree pvalue  p_{i} =  0.185 000

Degree assortativity  ρ =  −0.012 124 7

Degree assortativity pvalue  p_{ρ} =  2.147 62 × 10^{−7}

In/outdegree correlation  ρ^{±} =  +0.308 011

Clustering coefficient  c =  0.113 195

Directed clustering coefficient  c^{±} =  0.208 515

Spectral norm  α =  54.592 5

Operator 2norm  ν =  46.239 4

Cyclic eigenvalue  π =  15.718 2

Algebraic connectivity  a =  0.908 859

Reciprocity  y =  0.478 501

Nonbipartivity  b_{A} =  0.152 740

Normalized nonbipartivity  b_{N} =  0.435 186

Spectral bipartite frustration  b_{K} =  0.047 775 1

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]

V. Batagelj, A. Mrvar, and M. Zavešnik.
Network analysis of texts.
In Language Technologies, pages 143–148, 2002.
