Yeast

This undirected network contains protein interactions contained in yeast. Research showed that proteins with a high degree were more important for the surivial of the yeast than others. A node represents a protein and an edge represents a metabolic interaction between two proteins. The network contains loops.

Metadata

CodeMp
Internal namemoreno_propro
NameYeast
Data sourcehttp://moreno.ss.uci.edu/data.html#pro-pro
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Category
Metabolic network
Node meaningProtein
Edge meaningInteraction
Network formatUnipartite, undirected
Edge typeUnweighted, no multiple edges
LoopsContains loops

Statistics

Size n =1,870
Volume m =2,277
Loop count l =74
Wedge count s =12,107
Claw count z =75,064
Cross count x =611,457
Triangle count t =222
Square count q =461
4-Tour count T4 =56,522
Maximum degree dmax =56
Average degree d =2.435 29
Fill p =0.001 301 60
Size of LCC N =1,458
Diameter δ =19
50-Percentile effective diameter δ0.5 =6.390 63
90-Percentile effective diameter δ0.9 =9.589 12
Median distance δM =7
Mean distance δm =7.069 74
Gini coefficient G =0.449 043
Balanced inequality ratio P =0.328 063
Relative edge distribution entropy Her =0.943 033
Power law exponent γ =2.869 77
Tail power law exponent γt =3.041 00
Tail power law exponent with p γ3 =3.041 00
p-value p =0.611 000
Degree assortativity ρ =−0.161 530
Degree assortativity p-value pρ =3.843 25 × 10−27
Clustering coefficient c =0.055 009 5
Spectral norm α =7.561 90
Algebraic connectivity a =0.021 259 3
Spectral separation |λ1[A] / λ2[A]| =1.006 17
Non-bipartivity bA =0.006 130 51
Normalized non-bipartivity bN =0.015 761 1
Algebraic non-bipartivity χ =0.028 868 1
Spectral bipartite frustration bK =0.002 639 85
Controllability C =614
Relative controllability Cr =0.328 342

Plots

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

SynGraphy

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] Hawoong Jeong, Sean P Mason, A-L Barabási, and Zoltan N Oltvai. Lethality and centrality in protein networks. Nature, 411(6833):41–42, 2001.
[3] Stéphane Coulomb, Michel Bauer, Denis Bernard, and Marie-Claude Marsolier-Kergoat. Gene essentiality and the topology of protein interaction networks. Proc. of the Royal Soc. B: Biol. Sci., 272(1573):1721–1725, 2005.
[4] Jing-Dong J. Han, Denis Dupuy, Nicolas Bertin, Michael E. Cusick, and Marc Vidal. Effect of sampling on topology predictions of protein-protein interaction networks. Nature Biotechnol., 23(7):839–844, 2005.
[5] Michael P. H. Stumpf, Carsten Wiuf, and Robert M. May. Subnets of scale-free networks are not scale-free: Sampling properties of networks. Proc. Natl. Acad. Sci. U.S.A., 102(12):4221–4224, 2005.