# netrankr v0.2.1

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## 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>.

# netrankr

## Overview

netrankr is an R package to analyze partial rankings in the context of networks centrality. While the package includes the possibility to build a variety of indices, its main focus lies on index-free assessment of centrality. Computed partial rankings can be analyzed with a variety of methods. These include probabilistic methods like computing expected node ranks and relative rank probabilities (how likely is it that a node is more central than another?).

Most implemented methods are very general and can be used whenever partial rankings have to be analysed.

Visit the online manual for more Details.

## Install

To install from CRAN:

 install.packages("netrankr")


To install the developer version from github:

#install.packages(devtools)
devtools::install_github("schochastics/netrankr")


## Details

Check out the online manual for more help.

The core functions of the package are:

• Computing the neighborhood inclusion preorder with neighborhood_inclusion(). The resulting partial ranking is the foundation for any centrality related analysis on undirected and unweighted graphs. More details can be found in the dedicated vignette: vignette("neighborhood_inclusion",package="netrankr"). A generalizded version of neighborhood inclusion is implemented in positional_dominance(). See vignette("positional_dominance",package="netrankr") for help.

• Constructing graphs with a unique centrality ranking with threshold_graph(). This class of graphs, known as threshold graphs, can be used to benchmark centrality indices, since they only allow for one ranking of the nodes. For more details consult the vignette: vignette("threshold_graph",package="netrankr").

• Computing probabilistic centrality rankings. The package includes several function to calculate rank probabilities of nodes in a network, including expected ranks (how central do we expect a node to be?) and relative rank probabilities (how likely is it that a node is more central than another?). These probabilities can either be computed exactly for small networks (exact_rank_prob()), based on an almost uniform sample (mcmc_rank_prob()) or approximated via several heuristics (approx_rank_expected(),approx_rank_relative()). Consult vignette('probabilistic_cent',package='netrankr') for more information and vignette('benchmarks',package='netrankr') for applicability.

• Although the focus of the package lies on an index-free assessement of centrality, the package provides the possibility to build a variety of indices. Consult vignette('centrality_indices',package='netrankr') for more information.

The package includes several additional vignettes, which can be viewed with browseVignettes(package = "netrankr") or online

## Functions in netrankr

 Name Description hyperbolic_index Hyperbolic (centrality) index comparable_pairs Comparable pairs in a partial ranking neighborhood_inclusion Neighborhood-inclusion preorder threshold_graph Random threshold graphs spectral_gap Spectral gap of a graph mcmc_rank_prob Estimate rank probabilities with Markov Chains positional_dominance Generalized Dominance in Graphs rank_intervals Rank interval of nodes plot_rank_intervals Plot rank intervals is_preserved Check preservation majorization_gap Majorization gap index_builder Centrality Index Builder indirect_relations Indirect relations in a network transform_relations Transform indirect relations transitive_reduction Transitive Reduction exact_rank_prob Probabilistic centrality rankings florentine_m Florentine family marriage network approx_rank_relative Approximation of relative rank probabilities compare_ranks Count occurrences of pairs in rankings get_rankings Rankings that extend a partial ranking netrankr netrankr: An R package for centrality and partial rankings in networks aggregate_positions Quantification of (indirect) relations dominance_graph Partial ranking as directed graph approx_rank_expected Approximation of expected ranks No Results!

## Vignettes of netrankr

 Name benchmarks.Rmd centrality_indices.Rmd indirect_relations.Rmd mcmc_samples_exp.png mcmc_samples_rel.png neighborhood_inclusion.Rmd partial_centrality.Rmd positional_dominance.Rmd probabilistic_cent.Rmd quality_expected_cor.png quality_expected_mse.png quality_relative_mse.png quality_relative_mse2.png runtimes_exact.png runtimes_mcmc.png threshold_graph.Rmd use_case.Rmd No Results!

## Details

 Type Package URL https://schochastics.github.io/netrankr BugReports https://github.com/schochastics/netrankr/issues License MIT + file LICENSE Encoding UTF-8 LazyData true LinkingTo Rcpp,RcppArmadillo SystemRequirements C++11 RoxygenNote 6.0.1 VignetteBuilder knitr NeedsCompilation yes Packaged 2018-09-17 18:49:34 UTC; david Repository CRAN Date/Publication 2018-09-18 08:20:03 UTC
 suggests ggplot2 , knitr , magrittr , miniUI (>= 0.1.1) , rmarkdown , rstudioapi (>= 0.5) , shiny (>= 0.13) , testthat imports igraph (>= 1.0.1) , Rcpp (>= 0.12.8) depends R (>= 3.0.1) linkingto RcppArmadillo Contributors Julian Mller