Implementation of network diffusion algorithms such as heat diffusion or Markov random walks. Network diffusion algorithms generally spread information in the form of node weights along the edges of a graph to other nodes. These weights can for example be interpreted as temperature, an initial amount of water, the activation of neurons in the brain, or the location of a random surfer in the internet. The information (node weights) is iteratively propagated to other nodes until a equilibrium state or stop criterion occurs.
| Version: | 0.1.4 |
| Depends: | R (≥ 3.4) |
| Imports: | Rcpp, igraph, methods |
| LinkingTo: | Rcpp, RcppEigen |
| Suggests: | knitr, rmarkdown, testthat, lintr, Matrix |
| Published: | 2018-05-17 |
| Author: | Simon Dirmeier [aut, cre] |
| Maintainer: | Simon Dirmeier <simon.dirmeier at gmx.de> |
| BugReports: | https://github.com/dirmeier/diffusr/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/dirmeier/diffusr |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| Materials: | NEWS |
| CRAN checks: | diffusr results |
| Reference manual: | diffusr.pdf |
| Vignettes: |
The diffusr tutorial |
| Package source: | diffusr_0.1.4.tar.gz |
| Windows binaries: | r-devel: diffusr_0.1.4.zip, r-release: diffusr_0.1.4.zip, r-oldrel: diffusr_0.1.4.zip |
| macOS binaries: | r-release: diffusr_0.1.4.tgz, r-oldrel: diffusr_0.1.4.tgz |
| Old sources: | diffusr archive |
| Reverse imports: | perturbatr |
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