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.

Author

The influential package was written by Adrian (Abbas) 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("devtools")
devtools::install_github("asalavaty/influential")

Vignettes

Detailed description of the functions and their outputs

You may browse Vignettes from within R using the following code.

browseVignettes("influential")

An Example for Calculation of IVI

This is a basic example which shows you how to solve a common problem:

library(influential)

MyData <- centrality.measures # A data frame of centrality measures

# This function calculates the Integrated Value of Influence (IVI)
My.vertices.IVI <- ivi.from.indices(DC = centrality.measures$DC,       # Calculation of IVI
                                   CR = centrality.measures$CR,
                                   NC = centrality.measures$NC,
                                   LH_index = centrality.measures$LH_index,
                                   BC = centrality.measures$BC,
                                   CI = centrality.measures$CI)

print(head(My.vertices.IVI))
#> [1] 24.670056  8.344337 18.621049  1.017768 29.437028 33.512598

How to cite influential

To cite influential, please cite the associated paper. 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.