Description Usage Arguments Value Note Author(s) References See Also Examples
View source: R/computeModules.R
This function takes a bipartite weighted graph and computes modules by applying Newman's modularity measure in a bipartite weighted version to it.
metaComputeModules
reruns the algorithm several times, returning the most modular result, to stabilise modularity computation.
1 2 3 4  computeModules(web, method="Beckett", deep = FALSE, deleteOriginalFiles = TRUE,
steps = 1000000, tolerance = 1e10, experimental = FALSE, forceLPA=FALSE)
metaComputeModules(moduleObject, N=5, method="DormannStrauss", ...)

web 

method 
Choice between the algorithm(s) provided by Stephen Beckett (2016) or Dormann & Strauss (2016). Defaults to the much faster and in the majority of cases better algorithm of Beckett. (Note the optional argument forceLPA to use his slightly inferior but even faster pure LPA algorithm.) 
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.) 
moduleObject 
Output from running 
forceLPA 
Logical; should the even faster pure LPAalgorithm of Beckett be used? DIRTLPA, the default, is less likely to get trapped in a local minimum, but is slightly slower. Defaults to FALSE. 
N 
Number of replicate runs; defaults to 5. 
... 
Arguments passed on to 
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) or a small constant (1E5 or so) and it will work again.
When using the method DormannStrauss, files are written onto the hard drive during the computation. These files are by default deleted after the computation terminates, unless it breaks. Details of the modularity algorithm can be found in Dormann \& Strauß (2013).
Rouven Strauss, with fixes by Carsten Dormann and Tobias Hegemann; modified to accommodate Beckett's algorithm by Carsten Dormann
Beckett, S.J. 2016 Improved community detection in weighted bipartite networks. Royal Society open science 3, 140536.
Dormann, C. F., and R. Strauß. 2014. Detecting modules in quantitative bipartite networks: the QuanBiMo algorithm. Methods in Ecology & Evolution 5 90–98 (or arXiv [qbio.QM] 1304.3218.)
Liu X. & Murata T. 2010. An Efficient Algorithm for Optimizing Bipartite Modularity in Bipartite Networks. Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII) 14 408–415.
Newman M.E.J. 2004. Physical Review E 70 056131
See also class "moduleWeb", function "listModuleInformation", function "printoutModuleInformation", DIRT_LPA_wb_plus
.
1 2 3 4 5 6 7 8 9 10  ## Not run:
data(small1976)
(res < computeModules(small1976))
plotModuleWeb(res)
# slow:
res2 < metaComputeModules(small1976) # makes no sense for "Beckett"
res2
## End(Not run)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.