netrankr: Analyzing Partial Rankings in Networks

Implements methods for centrality related analyses of networks. While the package includes the possibility to build more than 20 indices, its main focus lies on index-free assessment of centrality via partial rankings obtained by neighborhood-inclusion or positional dominance. These partial rankings can be analyzed with different methods, including probabilistic methods like computing expected node ranks and relative rank probabilities (how likely is it that a node is more central than another?). The methodology is described in depth in the vignettes and in Schoch (2018) <doi:10.1016/j.socnet.2017.12.003>.

Version: 1.2.3
Depends: R (≥ 3.0.1)
Imports: igraph (≥ 1.0.1), Rcpp (≥ 0.12.8), Matrix
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, magrittr, testthat, shiny (≥ 0.13), miniUI (≥ 0.1.1), rstudioapi (≥ 0.5), covr
Published: 2023-12-19
DOI: 10.32614/CRAN.package.netrankr
Author: David Schoch ORCID iD [aut, cre], Julian Müller [ctb]
Maintainer: David Schoch <david at>
License: MIT + file LICENSE
NeedsCompilation: yes
Citation: netrankr citation info
Materials: README NEWS
CRAN checks: netrankr results


Reference manual: netrankr.pdf
Vignettes: 09 benchmarks
05 centrality indices
04 indirect relations in networks
01 neighborhood-inclusion and centrality
06 partial centrality
03 positional dominance in networks
07 probabilistic centrality
02 uniquely ranked graphs
08 use case


Package source: netrankr_1.2.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): netrankr_1.2.3.tgz, r-oldrel (arm64): netrankr_1.2.3.tgz, r-release (x86_64): netrankr_1.2.3.tgz, r-oldrel (x86_64): netrankr_1.2.3.tgz
Old sources: netrankr archive

Reverse dependencies:

Reverse depends: parsec
Reverse suggests: tidygraph


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