Identification and Classification of the Most Influential Nodes.
influential 
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 for a more complete description of the IVI formula and all of its underpinning methods and analyses.
Also, read our preprint on the ExIR model and its validations.
Author
The influential package was written by Adrian Salavaty
Advisors
Mirana Ramialison and Peter D. Currie
How to Install
You can install the official CRAN release of the influential with the following code:
install.packages("influential")
Or the development version from GitHub:
## 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.
You may browse Vignettes from within R using the following code.
browseVignettes("influential")
Shiny apps
IVI Shiny App: 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.
influential::runShinyApp("IVI")
- ExIR Shiny App: 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.
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).
You can also refer to the package’s citation information using the citation() function.
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.