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influential

Network influence analysis, centrality assessment, and experimental feature prioritization in R

Overview

influential is an R package for identifying influential nodes in networks and for classifying and prioritizing candidate features from experimental data. It brings together association analysis, network reconstruction, centrality assessment, influence ranking, visualization, simulation, and experimental feature prioritization in a single toolkit.

Two complementary workflows sit at the center of the package:

  • Integrated Value of Influence (IVI) integrates local, semi-local, and global topological information to identify influential network nodes while reducing the limitations of relying on any single centrality measure.
  • Experimental data-based Integrative Ranking (ExIR) combines experimental evidence, machine learning, network reconstruction, and influence ranking to classify and prioritize candidate drivers, biomarkers, and mediators from omics data.

The package also provides Hubness score for local network power, Spreading score for information-spreading potential, SIRIR for simulation-based influence ranking, computational knockout/up-regulation, fast correlation analysis, network reconstruction utilities, centrality measures, and centrality-based network visualization.

At a glance

CapabilityWhat influential provides
Association analysisFast Pearson/Spearman correlation analysis with optional mutual rank, p-values, and adjusted p-values
Network reconstructionConstruction of igraph networks from data frames, adjacency matrices, incidence matrices, and SIF files
Centrality analysisLocal, semi-local, and global centrality measures together with association assessment
Network influenceIVI, Hubness score, Spreading score, and SIRIR
Experimental prioritizationExIR-based classification and ranking of drivers, biomarkers, and mediators from omics data
Perturbation & visualizationIn silico knockout/up-regulation and centrality-based network visualization
Interactive analysisBrowser-based and locally launchable Shiny interfaces for IVI and ExIR

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Feature ExplorerExplore the features and capabilities of influential

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Install

install.packages('influential')

Monthly Downloads

1,035

Version

2.3.2

License

GPL-3

Issues

Pull Requests

Stars

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Maintainer

Adrian Salavaty

Last Published

August 23rd, 2026

Functions in influential (2.3.2)

hubness.score

Hubness score
influential-package

Influential package
exir.vis

Visualization of ExIR results
h_index

H-index
fcor

Fast correlation and mutual rank analysis
double.cent.assess

Assessment of innate features and associations of two network centrality measures (dependent and independent)
double.cent.assess.noRegression

Assessment of innate features and associations of two network centrality measures
igraph-reexports

Re-export selected igraph functions
exir

Experimental data-based Integrated Ranking
ivi

Integrated Value of Influence (IVI)
sif2igraph

SIF to igraph
reexports

Objects exported from other packages
spreading.score

Spreading score
lh_index

local H-index (LH-index)
neighborhood.connectivity

Neighborhood connectivity
sirir

SIR-based Influence Ranking
runShinyApp

Run shiny app
ivi.from.indices

Integrated Value of Influence (IVI)
centrality.measures

Centrality measures dataset
diff_data.assembly

Assembling the differential/regression data
betweenness

Vertex betweenness centrality
comp_manipulate

Computational manipulation of cells
collective.influence

Collective Influence (CI)
cond.prob.analysis

Conditional probability of deviation from means
clusterRank

ClusterRank (CR)
coexpression.data

Co-expression dataset
cent_network.vis

Centrality-based network visualization
coexpression.adjacency

Adjacency matrix