Condensed matter (2003)
These are scientific collaboration in the area of Condensed Matter (physics).
It contains coauthorships from papers uploaded to arXiv in the "condensed
matter" section, in the time range 1995 to 2003.
Metadata
Statistics
Size  n =  30,460

Volume  m =  120,029

Loop count  l =  0

Wedge count  s =  2,489,239

Claw count  z =  42,828,081

Cross count  x =  976,339,458

Triangle count  t =  232,994

Square count  q =  2,200,663

4Tour count  T_{4} =  27,802,318

Maximum degree  d_{max} =  202

Average degree  d =  7.881 09

Fill  p =  0.000 258 744

Size of LCC  N =  27,519

Diameter  δ =  16

50Percentile effective diameter  δ_{0.5} =  5.372 28

90Percentile effective diameter  δ_{0.9} =  7.195 30

Median distance  δ_{M} =  6

Mean distance  δ_{m} =  5.911 84

Gini coefficient  G =  0.519 927

Relative edge distribution entropy  H_{er} =  0.950 974

Power law exponent  γ =  1.631 95

Tail power law exponent  γ_{t} =  3.621 00

Tail power law exponent with p  γ_{3} =  3.621 00

pvalue  p =  0.059 000 0

Degree assortativity  ρ =  +0.178 214

Degree assortativity pvalue  p_{ρ} =  0.000 00

Clustering coefficient  c =  0.280 801

Spectral norm  α =  40.309 7

Algebraic connectivity  a =  0.027 584 2

Nonbipartivity  b_{A} =  0.607 353

Normalized nonbipartivity  b_{N} =  0.075 005 1

Spectral bipartite frustration  b_{K} =  0.003 792 04

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]

Mark E. J. Newman.
The structure of scientific collaboration networks.
Proc. Natl. Acad. Sci. U.S.A., 98(2):404–409, 2001.

[3]

Jordi Duch and Alex Arenas.
Community detection in complex networks using extremal optimization.
Phys. Rev. E, 72(2):027104, 2005.
