Java Development Kit
This is the software class dependency network of the JDK 1.6.0.7 framework. The
network is directed. Nodes represent classes. An edge between them indicates
that there exists a dependency between two classes. As there may be multiple
references between classes the network has multiple edges.
Metadata
Statistics
Size  n =  6,434

Volume  m =  150,985

Unique edge count  m̿ =  53,892

Loop count  l =  0

Wedge count  s =  52,676,393

Claw count  z =  92,489,410,361

Cross count  x =  131,184,856,673,035

Triangle count  t =  194,842

Square count  q =  82,893,262

4Tour count  T_{4} =  873,958,984

Maximum degree  d_{max} =  32,530

Maximum outdegree  d^{+}_{max} =  375

Maximum indegree  d^{−}_{max} =  32,507

Average degree  d =  46.933 5

Fill  p =  0.001 302 06

Average edge multiplicity  m̃ =  2.801 62

Size of LCC  N =  6,434

Size of LSCC  N_{s} =  77

Relative size of LSCC  N^{r}_{s} =  0.011 967 7

Diameter  δ =  7

50Percentile effective diameter  δ_{0.5} =  1.611 33

90Percentile effective diameter  δ_{0.9} =  2.473 00

Median distance  δ_{M} =  2

Mean distance  δ_{m} =  2.188 07

Gini coefficient  G =  0.750 504

Balanced inequality ratio  P =  0.201 772

Outdegree balanced inequality ratio  P_{+} =  0.266 801

Indegree balanced inequality ratio  P_{−} =  0.097 029 5

Relative edge distribution entropy  H_{er} =  0.829 550

Power law exponent  γ =  1.481 00

Tail power law exponent  γ_{t} =  2.361 00

Tail power law exponent with p  γ_{3} =  2.361 00

pvalue  p =  0.000 00

Outdegree tail power law exponent with p  γ_{3,o} =  2.821 00

Outdegree pvalue  p_{o} =  0.000 00

Indegree tail power law exponent with p  γ_{3,i} =  1.751 00

Indegree pvalue  p_{i} =  0.012 000 0

Degree assortativity  ρ =  −0.223 025

Degree assortativity pvalue  p_{ρ} =  0.000 00

In/outdegree correlation  ρ^{±} =  −0.031 772 5

Clustering coefficient  c =  0.011 096 5

Directed clustering coefficient  c^{±} =  0.454 164

Spectral norm  α =  1,062.14

Operator 2norm  ν =  1,045.24

Cyclic eigenvalue  π =  16.877 2

Algebraic connectivity  a =  0.401 218

Spectral separation  λ_{1}[A] / λ_{2}[A] =  1.032 18

Reciprocity  y =  0.008 684 03

Nonbipartivity  b_{A} =  0.031 173 4

Normalized nonbipartivity  b_{N} =  0.160 371

Algebraic nonbipartivity  χ =  0.319 511

Spectral bipartite frustration  b_{K} =  0.004 788 98

Controllability  C =  4,232

Relative controllability  C_{r} =  0.657 756

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 ]
