influential

Overview

The goal of influential is to help identification of the most influential nodes (hubs) in a network. This package contains functions for reconstruction of networks from adjacency matrices and data frames, analysis of the topology of the network and calculation of centrality measures. The first integrative method (i.e. the integrated hubness score (IHS) method) for the identification of network hubs is also provided as a function, which is the main purpose of the package. Also, neighborhood connectivity, one of the required centrality measures for the calculation of IHS, is for the first time calculable in the R environment. 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 (not published yet) for a more complete description of the IHS formula and all of its underpinning methods and analyses.

Author

The influential package was written by Adrian (Abbas) Salavaty

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

An Example for Calculation of IHS

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 Hubness Score (IHS)
My.vertices.IHS <- ihs(DC = centrality.measures$Degree,
                       BC = centrality.measures$BetweennessCentrality,
                       NC = centrality.measures$NeighborhoodConnectivity)

print(head(My.vertices.IHS))
#> [1] 196.2039215   2.9822273   0.1572078   6.1757221   0.3199993   0.5095222

How to cite influential

To cite influential, please cite the associated paper (unpublished yet). 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.