influential (version 2.2.9)

hubness.score: Hubness score

Description

This function calculates the Hubness score of the desired nodes from a graph. Hubness score reflects the power of each node in its surrounding environment and is one of the major components of the IVI.

Usage

hubness.score(
  graph,
  vertices = V(graph),
  directed = FALSE,
  mode = "all",
  loops = TRUE,
  scale = "range",
  verbose = FALSE
)

Value

A numeric vector with the Hubness scores.

Arguments

graph

A graph (network) of the igraph class.

vertices

A vector of desired vertices, which could be obtained by the V function.

directed

Logical scalar, whether to directed graph is analyzed. This argument is ignored for undirected graphs.

mode

The mode of Hubness score depending on the directedness of the graph. If the graph is undirected, the mode "all" should be specified. Otherwise, for the calculation of Hubness score based on incoming connections select "in" and for the outgoing connections select "out". Also, if all of the connections are desired, specify the "all" mode. Default mode is set to "all".

loops

Logical; whether the loop edges are also counted.

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 hub 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 hub 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(), ivi.from.indices(), ivi(), spreading.score()

Examples

if (FALSE) {
MyData <- coexpression.data
My_graph <- graph_from_data_frame(MyData)
GraphVertices <- V(My_graph)
Hubness.score <- hubness.score(graph = My_graph, vertices = GraphVertices,
                               directed = FALSE, mode = "all",
                               loops = TRUE, scale = "range")
}