influential: Identification and Classification of the Most Influential Nodes

Contains functions for the classification and ranking of top candidate features, reconstruction of networks from adjacency matrices and data frames, analysis of the topology of the network and calculation of centrality measures, and identification of the most influential nodes. Also, 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. Additionally, 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 direction. Fred Viole and David Nawrocki (2013, ISBN:1490523995). Csardi G, Nepusz T (2006). "The igraph software package for complex network research." InterJournal, Complex Systems, 1695. Adopted algorithms and sources are referenced in function document.

Version: 2.0.0
Depends: R (≥ 2.10)
Imports: igraph, ranger, coop, reshape2, ggplot2
Suggests: Hmisc (≥ 4.3-0), mgcv (≥ 1.8-31), nortest (≥ 1.0-4), NNS (≥, parallel, knitr, rmarkdown
Published: 2020-09-25
Author: Adrian (Abbas) Salavaty [aut, cre], Mirana Ramialison [ths], Peter D. Currie [ths]
Maintainer: Adrian Salavaty <abbas.salavaty at>
License: GPL-3
NeedsCompilation: no
Citation: influential citation info
Materials: README NEWS
CRAN checks: influential results


Reference manual: influential.pdf
Vignettes: Introduction to influential
Package source: influential_2.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: influential_2.0.0.tgz, r-oldrel: influential_2.0.0.tgz
Old sources: influential archive


Please use the canonical form to link to this page.