influential (version 2.2.9)

ivi.from.indices: Integrated Value of Influence (IVI)

Description

This function calculates the IVI of the desired nodes from previously calculated centrality measures. This function is not dependent to other packages and the required centrality measures, namely degree centrality, ClusterRank, betweenness centrality, Collective Influence, local H-index, and neighborhood connectivity could have been calculated by any means beforehand. A shiny app has also been developed for the calculation of IVI as well as IVI-based network visualization, which is accessible using the `influential::runShinyApp("IVI")` command. You can also access the shiny app online at https://influential.erc.monash.edu/.

Usage

ivi.from.indices(
  DC,
  CR,
  LH_index,
  NC,
  BC,
  CI,
  scale = "range",
  verbose = FALSE
)

Value

A numeric vector with the IVI values based on the provided centrality measures.

Arguments

DC

A vector containing the values of degree centrality of the desired vertices.

CR

A vector containing the values of ClusterRank of the desired vertices.

LH_index

A vector containing the values of local H-index of the desired vertices.

NC

A vector containing the values of neighborhood connectivity of the desired vertices.

BC

A vector containing the values of betweenness centrality of the desired vertices.

CI

A vector containing the values of Collective Influence of the desired vertices.

scale

Character string; the method used for scaling/normalizing the results. Options include 'range' (normalization within a 1-100 range), 'z-scale' (standardization using the z-score), and 'none' (no data scaling). The default selection is 'range'. Opting for the 'range' method is suitable when exploring a single network, allowing you to observe the complete spectrum and distribution of node influences. In this case, there is no intention to establish a specific threshold for the outcomes. However, it is possible to identify and present the top influential nodes based on their rankings. Conversely, the 'z-scale' option proves advantageous if the aim is to compare node influences across multiple networks or if there is a desire to establish a threshold (usually z-score > 1.645) for generating a list of the most influential nodes without manual intervention.

verbose

Logical; whether the accomplishment of different stages of the algorithm should be printed (default is FALSE).

See Also

cent_network.vis

Other integrative ranking functions: comp_manipulate(), exir(), hubness.score(), ivi(), spreading.score()

Examples

if (FALSE) {
MyData <- centrality.measures
My.vertices.IVI <- ivi.from.indices(DC = centrality.measures$DC,
                                    CR = centrality.measures$CR,
                                    NC = centrality.measures$NC,
                                    LH_index = centrality.measures$LH_index,
                                    BC = centrality.measures$BC,
                                    CI = centrality.measures$CI)
                                    }