https://github.com/asalavaty/influential
Identification and Classification of the Most Influential Nodes
https://github.com/asalavaty/influential
centrality-measures classification-model influence-ranking network-analysis priaritization-model r r-package
Last synced: 3 months ago
JSON representation
Identification and Classification of the Most Influential Nodes
- Host: GitHub
- URL: https://github.com/asalavaty/influential
- Owner: asalavaty
- License: gpl-3.0
- Created: 2020-01-16T03:08:37.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2025-07-21T22:49:49.000Z (6 months ago)
- Last Synced: 2025-10-22T03:56:37.951Z (3 months ago)
- Topics: centrality-measures, classification-model, influence-ranking, network-analysis, priaritization-model, r, r-package
- Language: R
- Homepage: https://asalavaty.github.io/influential/
- Size: 3.63 MB
- Stars: 29
- Watchers: 3
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# influential 
[](https://ci.appveyor.com/project/asalavaty/influential)
[](https://cran.r-project.org/package=influential)
[](https://cran.r-project.org/package=influential)
[](https://doi.org/10.1016/j.patter.2020.100052)
[](https://doi.org/10.1016/j.patter.2020.100052)
[](https://www.biorxiv.org/content/10.1101/2022.10.03.510585v1.abstract)
## Overview
The goal of `influential` is to help identification of the most
`influential` nodes in a network as well as the classification and
ranking of top candidate features. This package contains functions for
the classification and ranking of features, reconstruction of networks
from adjacency matrices and data frames, analysis of the topology of the
network and calculation of centrality measures as well as a novel and
powerful `influential` node ranking. The **Experimental data-based
Integrative Ranking (ExIR)** is a sophisticated model for classification
and ranking of the top candidate features based on only the experimental
data. The first integrative method, namely the **Integrated Value of
Influence (IVI)**, that captures all topological dimensions of the
network for the identification of network most `influential` nodes is
also provided as a function. Also, neighborhood connectivity, H-index,
local H-index, and collective influence (CI), all of which required
centrality measures for the calculation of **IVI**, are for the first
time provided in an R package. Additionally, a function is provided for
running **SIRIR** model, which is the combination of leave-one-out cross
validation technique and the conventional SIR model, on a network to
unsupervisedly rank the true influence of vertices. Furthermore, some
functions have been provided for the assessment of dependence and
correlation of two network centrality measures as well as the
conditional probability of deviation from their corresponding means in
opposite directions.
Check out [**our paper**](https://doi.org/10.1016/j.patter.2020.100052)
for a more complete description of the IVI formula and all of its
underpinning methods and analyses.
Also, read our
[**preprint**](https://www.biorxiv.org/content/10.1101/2022.10.03.510585v1.abstract)
on the ExIR model and its validations.
## Author
The `influential` package was written by [Adrian
Salavaty](https://asalavaty.com/)
## Advisors
Mirana Ramialison and Peter D. Currie
## How to Install
You can install the official [CRAN
release](https://cran.r-project.org/package=influential) of the
`influential` with the following code:
``` r
install.packages("influential")
```
Or the development version from GitHub:
``` r
## install.packages("remotes")
remotes::install_github("asalavaty/influential", build_vignettes = TRUE)
```
## Vignettes
A comprehensive introduction to `influential` and all of its functions
is available in the
[vignette](https://cran.r-project.org/package=influential/vignettes/Vignettes.html).
You may browse Vignettes from within R using the following code.
``` r
browseVignettes("influential")
```
## Shiny apps
- [Influential Software Package web
portal](https://influential.erc.monash.edu/)
- [IVI Shiny App](https://influential.erc.monash.edu/IVI/): A shiny app
for the calculation of the Integrated Value of Influence (IVI) of
network nodes as well as IVI-based visualization of the network.
You can also access the IVI shiny app offline from within R and run it
on your local machine using the following command.
``` r
influential::runShinyApp("IVI")
```
- [ExIR Shiny App](https://influential.erc.monash.edu/ExIR/): A shiny
app for running the Experimental-data-based Integrative Ranking (ExIR)
model as well as visualization of its results.
You can also access the ExIR shiny app offline from within R and run it
on your local machine using the following command.
``` r
influential::runShinyApp("ExIR")
```
## How to cite `influential`
To cite `influential`, please cite its associated paper:
- Integrated Value of Influence: An Integrative Method for the
Identification of the Most Influential Nodes within Networks. Adrian
Salavaty, Mirana Ramialison, Peter D Currie. *Patterns*. 2020.08.14
([Read online](https://doi.org/10.1016/j.patter.2020.100052)).
You can also refer to the package’s citation information using the
`citation()` function.
``` r
citation("influential")
```
## How to contribute
Please don’t hesitate to report any bugs/issues and request for
enhancement or any other contributions. To submit a bug report or
enhancement request, please use the [`influential` GitHub issues
tracker](https://github.com/asalavaty/influential/issues).