The document compares two biological networks, CRAN and BioConductor, in terms of their topological properties. It finds that CRAN has more nodes and edges than BioConductor, but BioConductor is more densely connected. A statistical test shows the networks have significantly different degree distributions, though both approximately follow power laws.

1. adevries@microsoft.com jrickert@microsoft.com

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7. *Note: Colour indicates communities found by the
walktrap algorithm, but has no common
meaning in the two networks

13. nodes edges average.path.length assortativity.degree no.clusters cluster.coef
cran 6867 14749 2.72 -0.082 1573 0.015
bioc 1552 5756 1.95 -0.078 70 0.060

14. Bootstrap sample: n = 1000, size of each subgraph = 500 nodes, no replacement
Two-sample Kolmogorov-Smirnov test
data: CRAN and BioConductor
D = 0.643, p-value < 2.2e-16
alternative hypothesis: two-sided

15. power.law.fit power.law.xmin power.law.KS.p
cran 2.55 5 0.061
bioc 2.59 9 0.632

16. Degree distribution of CRAN and BioConductor

17. Typical small sample n =100 P-value distribution

19. Formula: bioc_net ~ edges + degree(c(1, 2))