computeModules: Function "computeModules"

Description

This function takes a bipartite weighted graph and computes modules by applying M. E. J. Newman's modularity measure in a bipartite weighted version to it. During the computation files are written onto the hard drive disk. These files are by default deleted after the computation terminates. Details of the modularity algorithm can be found in Dormann & Strauß (2013).

Usage

computeModules(web, deep = FALSE, deleteOriginalFiles = TRUE, 
	steps = 1000000, tolerance = 1e-10, experimental = FALSE)

Arguments

web

web is the matrix representing the weighted bipartite graph (as an example, see e.g. web "small1976" in this package).

deep

If deep is set to FALSE (default), a flat clustering is computed, otherwise submodules are identified recursively within modules.

deleteOriginalFiles

If deleteOriginalFiles is set to TRUE (default), the files mentioned above in the description are deleted from the hard drive disk, otherwise not.

steps

steps is the number of steps after which the computation of modules stops if no better division into modules than the current one can be found.

tolerance

How small should the difference between MCMC-swap results be? At some point computer precision fluctuations make the algorithm fail to converge, which is why we choose a (very low) defaults of 1E-10.

experimental

Logical; using an undescribed and untested version for which no detail is available? (We suggest: not yet.)

Value

An object of class "moduleWeb" containing information about the computed modules. For details, please refer to the corresponding documentation file.

encoding

UTF-8

References

Dormann, C. F., and R. Strauß. 2013. Detecting modules in quantitative bipartite networks: the QuaBiMo algorithm. arXiv [q-bio.QM] 1304.3218.

Newman M.E.J. 2004. Physical Review E 70 056131

Examples

Run this code

data(small1976)
		(res <- computeModules(small1976)) # takes several minutes!
		plotModuleWeb(res)

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