JUNG/Javax
This is the software class dependency network of the JUNG 2.0.1 and javax
1.6.0.7 libraries, namespaces edu.uci.ics.jung and java/javax. 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 =  6,120

Volume  m =  138,706

Unique edge count  m̿ =  50,535

Loop count  l =  0

Wedge count  s =  49,825,722

Claw count  z =  85,378,501,686

Cross count  x =  117,750,526,945,905

Triangle count  t =  182,139

Square count  q =  80,032,387

4Tour count  T_{4} =  839,662,564

Maximum degree  d_{max} =  26,133

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

Maximum indegree  d^{−}_{max} =  26,110

Average degree  d =  45.328 8

Fill  p =  0.001 349 46

Average edge multiplicity  m̃ =  2.744 75

Size of LCC  N =  6,120

Size of LSCC  N_{s} =  77

Relative size of LSCC  N^{r}_{s} =  0.012 581 7

Diameter  δ =  7

50Percentile effective diameter  δ_{0.5} =  1.534 08

90Percentile effective diameter  δ_{0.9} =  1.963 50

Median distance  δ_{M} =  2

Mean distance  δ_{m} =  2.064 48

Gini coefficient  G =  0.752 594

Balanced inequality ratio  P =  0.204 468

Outdegree balanced inequality ratio  P_{+} =  0.263 702

Indegree balanced inequality ratio  P_{−} =  0.100 421

Relative edge distribution entropy  H_{er} =  0.825 325

Power law exponent  γ =  1.487 25

Tail power law exponent with p  γ_{3} =  2.341 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} =  1.781 00

Indegree pvalue  p_{i} =  0.034 000 0

Degree assortativity  ρ =  −0.232 705

Degree assortativity pvalue  p_{ρ} =  0.000 00

In/outdegree correlation  ρ^{±} =  −0.005 123 21

Clustering coefficient  c =  0.010 966 6

Directed clustering coefficient  c^{±} =  0.450 877

Spectral norm  α =  913.941

Operator 2norm  ν =  893.925

Algebraic connectivity  a =  0.401 225

Reciprocity  y =  0.009 696 25

Controllability  C =  4,057

Relative controllability  C_{r} =  0.662 908

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]

Lovro Šubelj and Marko Bajec.
Software systems through complex networks science: Review, analysis
and applications.
In Proc. Int. Workshop on Software Min., pages 9–16, 2012.
