This is the software class dependency network of the JUNG 2.0.1 and javax 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.


Internal namesubelj_jung-j
Data sourcehttp://lovro.lpt.fri.uni-lj.si/support.jsp
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 =6,120
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
4-Tour count T4 =839,662,564
Maximum degree dmax =26,133
Maximum outdegree d+max =375
Maximum indegree dmax =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 Ns =77
Relative size of LSCC Nrs =0.012 581 7
Diameter δ =7
50-Percentile effective diameter δ0.5 =1.534 08
90-Percentile effective diameter δ0.9 =1.963 50
Median distance δM =2
Mean distance δm =2.064 48
Gini coefficient G =0.752 594
Relative edge distribution entropy Her =0.825 325
Power law exponent γ =1.487 25
Tail power law exponent γt =2.341 00
Tail power law exponent with p γ3 =2.341 00
p-value p =0.000 00
Outdegree tail power law exponent with p γ3,o =2.821 00
Outdegree p-value po =0.000 00
Indegree tail power law exponent with p γ3,i =1.781 00
Indegree p-value pi =0.029 000 0
Degree assortativity ρ =−0.232 705
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =−0.005 123 21
Clustering coefficient c =0.010 966 6
Spectral norm α =913.941
Operator 2-norm ν =893.925
Cyclic eigenvalue π =16.877 2
Algebraic connectivity a =0.401 225
Spectral separation 1[A] / λ2[A]| =1.044 74
Reciprocity y =0.009 696 25
Non-bipartivity bA =0.042 822 8
Normalized non-bipartivity bN =0.162 291
Spectral bipartite frustration bK =0.004 860 33
Controllability C =4,057
Relative controllability Cr =0.662 909


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 ]
[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.