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netrankr

netrankr: An R package for total, partial, and probabilistic rankings in networks - Published in JOSS (2022)

https://github.com/schochastics/netrankr

Science Score: 100.0%

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Keywords

network-analysis network-centrality r-package

Keywords from Contributors

ggraph graph-algorithms network-visualization

Scientific Fields

Medicine Life Sciences - 40% confidence
Psychology Social Sciences - 40% confidence
Last synced: 6 months ago · JSON representation ·

Repository

An R package for network centrality

Basic Info
Statistics
  • Stars: 49
  • Watchers: 5
  • Forks: 5
  • Open Issues: 0
  • Releases: 9
Topics
network-analysis network-centrality r-package
Created almost 9 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.Rmd 

---
output: github_document
---

# netrankr 
[![R-CMD-check](https://github.com/schochastics/netrankr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/schochastics/netrankr/actions/workflows/R-CMD-check.yaml)
[![CRAN Status Badge](https://www.r-pkg.org/badges/version/netrankr)](https://cran.r-project.org/package=netrankr)
[![CRAN Downloads](https://cranlogs.r-pkg.org/badges/netrankr)](https://CRAN.R-project.org/package=netrankr) 
[![Codecov test coverage](https://codecov.io/gh/schochastics/netrankr/branch/main/graph/badge.svg)](https://app.codecov.io/gh/schochastics/netrankr?branch=main)
[![JOSS](https://joss.theoj.org/papers/10.21105/joss.04563/status.svg)](https://doi.org/10.21105/joss.04563)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7109041.svg)](https://doi.org/10.5281/zenodo.7109041)


```{r setup, include=FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    fig.align = "center",
    out.width = "80%",
    comment = "#>",
    fig.path = "man/figures/README-",
    echo = TRUE,
    warning = FALSE,
    message = FALSE
)
```

## Overview 

The literature is flooded with centrality indices and new ones are introduced 
on a regular basis. Although there exist several theoretical and empirical guidelines 
on when to use certain indices, there still exists plenty of ambiguity in the concept 
of network centrality. To date, network centrality is nothing more than applying indices
to a network:

![](man/figures/flow_old.png)

The only degree of freedom is the choice of index. The package comes with an Rstudio addin (`index_builder()`),
which allows to build or choose from more than 20 different indices. Blindly (ab)using
this function is highly discouraged!

The `netrankr` package is based on the idea that centrality is more than a 
conglomeration of indices. Decomposing them in a series of microsteps offers 
the posibility to gradually add ideas about centrality, without succumbing to
trial-and-error approaches. Further, it allows for alternative assessment methods
which can be more general than the index-driven approach:

![](man/figures/flow_new.png)

The new approach is centered around the concept of *positions*, which are defined as 
the relations and potential attributes of a node in a network. The aggregation
of the relations leads to the definition of indices. However, positions can also
be compared via *positional dominance*, leading to partial centrality rankings and
the option to calculate probabilistic centrality rankings.

For a more detailed theoretical background, consult the [Literature](#literature) 
at the end of this page.

________________________________________________________________________________

## Installation

To install from CRAN: 
```{r install_cran, eval=FALSE}
install.packages("netrankr")
```

To install the developer version from github:
```{r install_git, eval=FALSE}
# install.packages("remotes")
remotes::install_github("schochastics/netrankr")
```

________________________________________________________________________________

## Simple Example

This example briefly explains some of the functionality of the package and the 
difference to an index driven approach. For a more realistic application see 
the use case vignette. 

We work with the following small graph.
```{r example_graph, warning=FALSE,message=FALSE}
library(igraph)
library(netrankr)

data("dbces11")
g <- dbces11
```

```{r dbces_neutral, echo=FALSE}
library(ggraph)
V(g)$name <- as.character(1:11)
ggraph(g, "stress") +
    geom_edge_link0(edge_colour = "grey66") +
    geom_node_point(shape = 21, fill = "grey25", size = 8) +
    geom_node_text(aes(label = name), col = "white") +
    theme_graph()
```

Say we are interested in the most central node of the graph and simply compute some 
standard centrality scores with the `igraph` package. Defining centrality indices
in the `netrankr` package is explained in the centrality indices vignette.
```{r cent,warning=FALSE}
cent_scores <- data.frame(
    degree = degree(g),
    betweenness = round(betweenness(g), 4),
    closeness = round(closeness(g), 4),
    eigenvector = round(eigen_centrality(g)$vector, 4),
    subgraph = round(subgraph_centrality(g), 4)
)

# What are the most central nodes for each index?
apply(cent_scores, 2, which.max)
```

```{r dbces_color, echo=FALSE}
V(g)$col <- "none"
V(g)$col[apply(cent_scores, 2, which.max)] <- names(apply(cent_scores, 2, which.max))

V(g)$lab <- ""
V(g)$lab[apply(cent_scores, 2, which.max)] <- stringr::str_to_upper(stringr::str_extract(names(apply(cent_scores, 2, which.max)), "^[a-z]"))
ggraph(g, "stress") +
    geom_edge_link0(edge_colour = "grey66") +
    geom_node_point(aes(fill = col), shape = 21, size = 8) +
    geom_node_text(aes(label = lab), col = "white") +
    scale_fill_manual(values = c("#1874CD", "#CD2626", "#EEB422", "#9A32CD", "#4D4D4D", "#EE30A7")) +
    theme_graph() +
    theme(legend.position = "none")
```

As you can see, each index assigns the highest value to a different vertex.

A more general assessment starts by calculating the neighborhood inclusion preorder.
```{r ex_ni}
P <- neighborhood_inclusion(g)
P
```
[Schoch & Brandes (2016)](https://doi.org/10.1017/S0956792516000401) showed that
`P[u,v]=1` implies that u is less central than v for 
centrality indices which are defined via specific path algebras. These include
many of the well-known measures like closeness (and variants), betweenness (and variants)
as well as many walk-based indices (eigenvector and subgraph centrality, total communicability,...).

Neighborhood-inclusion defines a partial ranking on the set of nodes. Each ranking 
that is in accordance with this partial ranking yields a proper centrality ranking.
Each of these ranking can thus potentially be the outcome of a centrality index.  

Using rank intervals, we can examine the minimal and maximal possible rank of each node.
The bigger the intervals are, the more freedom exists for indices to rank nodes differently.
```{r partial}
plot(rank_intervals(P), cent_scores = cent_scores, ties.method = "average")
```

The potential ranks of nodes are not uniformly distributed in the intervals. To get
the exact probabilities, the function `exact_rank_prob()` can be used.
```{r ex_p}
res <- exact_rank_prob(P)
res
```
For the graph `g` we can therefore come up with
`r format(res$lin.ext,big.mark = ",")` indices that would rank the nodes differently.  

`rank.prob` contains the probabilities for each node to occupy a certain rank. 
For instance, the probability for each node to be the most central one is as follows.
```{r most_central}
round(res$rank.prob[, 11], 2)
```

`relative.rank` contains the relative rank probabilities. An entry `relative.rank[u,v]`
indicates how likely it is that `v` is more central than `u`.
```{r rel_rank}
# How likely is it, that 6 is more central than 3?
round(res$relative.rank[3, 6], 2)
```

`expected.ranks` contains the expected centrality ranks for all nodes. They are 
derived on the basis of `rank.prob`.
```{r exp_rank}
round(res$expected.rank, 2)
```
The higher the value, the more central a node is expected to be.

**Note**: The set of rankings grows exponentially in the number of nodes and the exact 
calculation becomes infeasible quite quickly and approximations need to be used.
Check the benchmark results for guidelines.

________________________________________________________________________________

## Theoretical Background {#literature}

`netrankr` is based on a series of papers that appeared in recent years. If you 
want to learn more about the theoretical background of the package, 
consult the following literature:

> Schoch, David. (2018). Centrality without Indices: Partial rankings and rank
Probabilities in networks. *Social Networks*, **54**, 50-60.([link](https://doi.org/10.1016/j.socnet.2017.12.003))

> Schoch, David & Valente, Thomas W., & Brandes, Ulrik. (2017). Correlations among centrality indices
and a class of uniquely ranked graphs. *Social Networks*, **50**, 46-54.([link](https://doi.org/10.1016/j.socnet.2017.03.010))

> Schoch, David & Brandes, Ulrik. (2016). Re-conceptualizing centrality in social networks.
*European Journal of Appplied Mathematics*, **27**(6), 971–985.
([link](https://doi.org/10.1017/S0956792516000401))

> Brandes, Ulrik. (2016). Network Positions.
*Methodological Innovations*, **9**, 2059799116630650.
([link](https://dx.doi.org/10.1177/2059799116630650))

## Code of Conduct

Please note that the netrankr project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

Owner

  • Name: David Schoch
  • Login: schochastics
  • Kind: user
  • Location: Germany
  • Company: cynkra

Data Scientist/DevOps Engineer at cynkra and #RStats developer

JOSS Publication

netrankr: An R package for total, partial, and probabilistic rankings in networks
Published
September 26, 2022
DOI
Volume 7, Issue 77, Page 4563
Authors
David Schoch ORCID
GESIS - Leibniz Institute for the Social Sciences
Editor
Mehmet Hakan Satman ORCID
Tags
network analysis network centrality partial orders
View PDF Review Thread Software Archive

Citation (CITATION.cff) 

# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "netrankr" in publications use:'
type: software
license: MIT
title: 'netrankr: Analyzing Partial Rankings in Networks'
version: 1.2.3
identifiers:
- type: doi
  value: 10.32614/CRAN.package.netrankr
- type: url
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abstract: 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) <https://doi.org/10.1016/j.socnet.2017.12.003>.
authors:
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  given-names: David
  email: david@schochastics.net
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preferred-citation:
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keywords:
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- r-package
references:
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  title: 'R: A Language and Environment for Statistical Computing'
  notes: Depends
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  institution:
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    address: Vienna, Austria
  year: '2024'
  version: '>= 3.0.1'
- type: software
  title: igraph
  abstract: 'igraph: Network Analysis and Visualization'
  notes: Imports
  url: https://r.igraph.org/
  repository: https://CRAN.R-project.org/package=igraph
  authors:
  - family-names: Csárdi
    given-names: Gábor
    email: csardi.gabor@gmail.com
    orcid: https://orcid.org/0000-0001-7098-9676
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    given-names: Tamás
    email: ntamas@gmail.com
    orcid: https://orcid.org/0000-0002-1451-338X
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    given-names: Vincent
    orcid: https://orcid.org/0000-0003-3170-3879
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    given-names: Szabolcs
    email: szhorvat@gmail.com
    orcid: https://orcid.org/0000-0002-3100-523X
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    given-names: Fabio
    email: fabio.zanini@unsw.edu.au
    orcid: https://orcid.org/0000-0001-7097-8539
  - family-names: Noom
    given-names: Daniel
  - family-names: Müller
    given-names: Kirill
    email: kirill@cynkra.com
    orcid: https://orcid.org/0000-0002-1416-3412
  year: '2024'
  doi: 10.32614/CRAN.package.igraph
  version: '>= 1.0.1'
- type: software
  title: Rcpp
  abstract: 'Rcpp: Seamless R and C++ Integration'
  notes: Imports
  url: https://www.rcpp.org
  repository: https://CRAN.R-project.org/package=Rcpp
  authors:
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    given-names: Dirk
    email: edd@debian.org
    orcid: https://orcid.org/0000-0001-6419-907X
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    orcid: https://orcid.org/0000-0002-2444-4226
  - family-names: Allaire
    given-names: JJ
    orcid: https://orcid.org/0000-0003-0174-9868
  - family-names: Ushey
    given-names: Kevin
    orcid: https://orcid.org/0000-0003-2880-7407
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    given-names: Qiang
    orcid: https://orcid.org/0000-0001-6786-5453
  - family-names: Russell
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    given-names: Iñaki
    orcid: https://orcid.org/0000-0001-6403-5550
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    given-names: Doug
    orcid: https://orcid.org/0000-0001-8316-9503
  - family-names: Chambers
    given-names: John
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  doi: 10.32614/CRAN.package.Rcpp
  version: '>= 0.12.8'
- type: software
  title: Matrix
  abstract: 'Matrix: Sparse and Dense Matrix Classes and Methods'
  notes: Imports
  url: https://R-forge.R-project.org/tracker/?atid=294&group_id=61
  repository: https://CRAN.R-project.org/package=Matrix
  authors:
  - family-names: Bates
    given-names: Douglas
    orcid: https://orcid.org/0000-0001-8316-9503
  - family-names: Maechler
    given-names: Martin
    email: mmaechler+Matrix@gmail.com
    orcid: https://orcid.org/0000-0002-8685-9910
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    given-names: Mikael
    orcid: https://orcid.org/0000-0002-3542-2938
  year: '2024'
  doi: 10.32614/CRAN.package.Matrix
- type: software
  title: Rcpp
  abstract: 'Rcpp: Seamless R and C++ Integration'
  notes: Imports
  url: https://www.rcpp.org
  repository: https://CRAN.R-project.org/package=Rcpp
  authors:
  - family-names: Eddelbuettel
    given-names: Dirk
    email: edd@debian.org
    orcid: https://orcid.org/0000-0001-6419-907X
  - family-names: Francois
    given-names: Romain
    orcid: https://orcid.org/0000-0002-2444-4226
  - family-names: Allaire
    given-names: JJ
    orcid: https://orcid.org/0000-0003-0174-9868
  - family-names: Ushey
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  - family-names: Kou
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    orcid: https://orcid.org/0000-0001-6786-5453
  - family-names: Russell
    given-names: Nathan
  - family-names: Ucar
    given-names: Iñaki
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  - family-names: Bates
    given-names: Doug
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  abstract: 'RcppArmadillo: ''Rcpp'' Integration for the ''Armadillo'' Templated Linear
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  - family-names: Eddelbuettel
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  - family-names: Bates
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  - family-names: Ni
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  authors:
  - family-names: Xie
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    given-names: JJ
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  - family-names: Cheng
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  - family-names: Chang
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  - family-names: Iannone
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  - family-names: Bache
    given-names: Stefan Milton
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  authors:
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  authors:
  - family-names: Chang
    given-names: Winston
    email: winston@posit.co
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  - family-names: Cheng
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  - family-names: Allaire
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  - family-names: Xie
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    given-names: Jeff
  - family-names: McPherson
    given-names: Jonathan
    email: jonathan@posit.co
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    given-names: Alan
  - family-names: Borges
    given-names: Barbara
  year: '2024'
  doi: 10.32614/CRAN.package.shiny
  version: '>= 0.13'
- type: software
  title: miniUI
  abstract: 'miniUI: Shiny UI Widgets for Small Screens'
  notes: Suggests
  repository: https://CRAN.R-project.org/package=miniUI
  authors:
  - family-names: Cheng
    given-names: Joe
    email: joe@rstudio.com
  year: '2024'
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- type: software
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  abstract: 'rstudioapi: Safely Access the RStudio API'
  notes: Suggests
  url: https://rstudio.github.io/rstudioapi/
  repository: https://CRAN.R-project.org/package=rstudioapi
  authors:
  - family-names: Ushey
    given-names: Kevin
    email: kevin@rstudio.com
  - family-names: Allaire
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  - family-names: Wickham
    given-names: Hadley
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  - family-names: Ritchie
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  version: '>= 0.5'

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Raniere Silva R****a@g****g 1
Committer Domains (Top 20 + Academic)
gesis.org: 2 ciencias.unam.mx: 1 schochastics.net: 1 uni-konstanz.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 19
  • Total pull requests: 9
  • Average time to close issues: 12 months
  • Average time to close pull requests: 4 days
  • Total issue authors: 6
  • Total pull request authors: 5
  • Average comments per issue: 0.95
  • Average comments per pull request: 0.22
  • Merged pull requests: 9
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 5
  • Average time to close issues: N/A
  • Average time to close pull requests: 6 days
  • Issue authors: 0
  • Pull request authors: 3
  • Average comments per issue: 0
  • Average comments per pull request: 0.4
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • corybrunson (8)
  • schochastics (7)
  • barracuda156 (1)
  • tedmoorman (1)
  • mbojan (1)
  • TomKellyGenetics (1)
Pull Request Authors
  • schochastics (3)
  • ArthurMuehl (2)
  • jbytecode (2)
  • luisDVA (2)
  • rgaiacs (1)
Top Labels
Issue Labels
enhancement (3) bug (2)
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads:
    • cran 1,150 last-month
  • Total docker downloads: 56
  • Total dependent packages: 2
    (may contain duplicates)
  • Total dependent repositories: 2
    (may contain duplicates)
  • Total versions: 17
  • Total maintainers: 1
cran.r-project.org: netrankr

Analyzing Partial Rankings in Networks

  • Versions: 11
  • Dependent Packages: 2
  • Dependent Repositories: 2
  • Downloads: 1,150 Last month
  • Docker Downloads: 56
Rankings
Stargazers count: 7.1%
Downloads: 10.6%
Average: 12.9%
Dependent packages count: 13.7%
Forks count: 14.2%
Dependent repos count: 19.2%
Maintainers (1)
david@schochastics.net
Last synced: 6 months ago
conda-forge.org: r-netrankr
  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 34.0%
Stargazers count: 38.3%
Average: 44.4%
Dependent packages count: 51.2%
Forks count: 54.2%
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.0.1 depends
  • Matrix * imports
  • Rcpp >= 0.12.8 imports
  • igraph >= 1.0.1 imports
  • knitr * suggests
  • magrittr * suggests
  • miniUI >= 0.1.1 suggests
  • rmarkdown * suggests
  • rstudioapi >= 0.5 suggests
  • shiny >= 0.13 suggests
  • testthat * suggests
.github/workflows/R-CMD-check.yaml actions
  • actions/checkout v3 composite
  • r-lib/actions/check-r-package v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
.github/workflows/test-coverage.yaml actions
  • actions/checkout v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite