influential
type: Item
instance of: R package
software version identifier: 2.2.6
software version identifier: 2.2.7
software version identifier: 0.1.0
software version identifier: 1.0.0
software version identifier: 1.1.0
software version identifier: 1.1.1
software version identifier: 1.1.2
software version identifier: 2.0.0
software version identifier: 2.0.1
software version identifier: 2.2.0
software version identifier: 2.2.1
software version identifier: 2.2.2
software version identifier: 2.2.3
software version identifier: 2.2.4
software version identifier: 2.2.5
software version identifier: 2.2.8
software version identifier: 2.2.9
CRAN project: influential
programmed in: R
last update: 2023-12-09T00:00:00Z
maintained by: Adrian Salavaty
description: 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.
author: Abbas Salavaty
copyright license: GNU General Public License, version 3.0
imports: igraph
imports: janitor
imports: ranger
imports: coop
imports: foreach
imports: doParallel
imports: data.table
imports: ggplot2
imports: BiocManager
depends on software: R
MaRDI profile type: Software
description: Identification and Classification of the Most Influential Nodes