Java Development Kit

This is the software class dependency network of the JDK 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.


Internal namesubelj_jdk
NameJava Development Kit
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Software network
Node meaningClass
Edge meaningDependency
Network formatUnipartite, directed
Edge typeUnweighted, multiple edges
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsDoes not contain loops


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
4-Tour count T4 =873,958,984
Maximum degree dmax =32,530
Maximum outdegree d+max =375
Maximum indegree dmax =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 Ns =77
Relative size of LSCC Nrs =0.011 967 7
Diameter δ =7
50-Percentile effective diameter δ0.5 =1.611 33
90-Percentile effective diameter δ0.9 =2.473 00
Median distance δM =2
Mean distance δm =2.188 07
Gini coefficient G =0.750 504
Relative edge distribution entropy Her =0.829 550
Power law exponent γ =1.481 00
Tail power law exponent γt =2.361 00
Degree assortativity ρ =−0.223 025
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =−0.031 772 5
Clustering coefficient c =0.011 096 5
Spectral norm α =1,062.14
Operator 2-norm ν =1,045.24
Cyclic eigenvalue π =16.877 2
Algebraic connectivity a =0.401 218
Spectral separation 1[A] / λ2[A]| =1.032 18
Reciprocity y =0.008 684 03
Non-bipartivity bA =0.031 173 4
Normalized non-bipartivity bN =0.160 371
Spectral bipartite frustration bK =0.004 788 98
Controllability C =4,232
Relative controllability Cr =0.657 756


Fruchterman–Reingold graph drawing

Degree distribution

Cumulative degree distribution

Lorenz curve

Spectral distribution of the adjacency matrix

Spectral distribution of the normalized adjacency matrix

Spectral distribution of the Laplacian

Spectral graph drawing based on the adjacency matrix

Spectral graph drawing based on the Laplacian

Spectral graph drawing based on the normalized adjacency matrix

Degree assortativity

Zipf plot

Hop distribution

Double Laplacian graph drawing

Delaunay graph drawing

In/outdegree scatter plot

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree distribution


Matrix decompositions plots



[1] Jérôme Kunegis. KONECT – The Koblenz Network Collection. In Proc. Int. Conf. on World Wide Web Companion, pages 1343–1350, 2013. [ http ]