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).
1 2  computeModules(web, deep = FALSE, deleteOriginalFiles = TRUE,
steps = 1000000, tolerance = 1e10, experimental = FALSE)

web 

deep 
If 
deleteOriginalFiles 
If 
steps 

tolerance 
How small should the difference between MCMCswap results be? At some point computer precision fluctuations make the algorithm fail to converge, which is why we choose a (very low) defaults of 1E10. 
experimental 
Logical; using an undescribed and untested version for which no detail is available? (We suggest: not yet.) 
An object of class "moduleWeb" containing information about the computed modules. For details, please refer to the corresponding documentation file.
For perfectly compartmentalised networks the algorithm may throw an error message. Please add a little bit of noise (e.g. uniform between 0 and 1 or so) and it will work again.
Rouven Strauss, with fixes by Carsten Dormann and Tobias Hegemann
Dormann, C. F., and R. Strauß. 2013. Detecting modules in quantitative bipartite networks: the QuaBiMo algorithm. arXiv [qbio.QM] 1304.3218.
Newman M.E.J. 2004. Physical Review E 70 056131
See also class "moduleWeb", function "listModuleInformation", function "printoutModuleInformation".
1 2 3 4 5 6  ## Not run:
data(small1976)
(res < computeModules(small1976)) # takes several minutes!
plotModuleWeb(res)
## End(Not run)

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