Network science

This is a network of co-authorships in the area of network science.


Internal namedimacs10-netscience
NameNetwork science
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Co-authorship network
Dataset timestamp 2006
Node meaningAuthor
Edge meaningCo-authorship
Network formatUnipartite, undirected
Edge typeUnweighted, no multiple edges
LoopsDoes not contain loops
Join Is the join of an underlying network
Multiplicity Does not have multiple edges, but the underlying data has


Size n =1,461
Volume m =2,742
Loop count l =0
Wedge count s =16,284
Claw count z =57,925
Cross count x =221,122
Triangle count t =3,764
Square count q =22,787
4-Tour count T4 =252,916
Maximum degree dmax =34
Average degree d =3.753 59
Fill p =0.002 570 95
Size of LCC N =379
Diameter δ =17
50-Percentile effective diameter δ0.5 =5.642 87
90-Percentile effective diameter δ0.9 =9.182 06
Median distance δM =6
Mean distance δm =6.285 28
Gini coefficient G =0.415 264
Relative edge distribution entropy Her =0.957 949
Power law exponent γ =1.970 78
Tail power law exponent γt =3.611 00
Tail power law exponent with p γ3 =3.611 00
p-value p =0.006 000 00
Degree assortativity ρ =+0.461 622
Degree assortativity p-value pρ =1.231 55 × 10−287
Clustering coefficient c =0.693 441
Spectral norm α =19.023 8
Algebraic connectivity a =0.015 203 8
Non-bipartivity bA =0.712 488
Normalized non-bipartivity bN =0.255 201
Algebraic non-bipartivity χ =0.462 123
Spectral bipartite frustration bK =0.023 953 0


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

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] Mark E. J. Newman. Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E, 74(3), 2006.