influential (version 2.2.9)

clusterRank: ClusterRank (CR)

Description

This function calculates the ClusterRank of input vertices and works with both directed and undirected networks. This function and all of its descriptions have been adapted from the centiserve package with some minor modifications. ClusterRank is a local ranking algorithm which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient.

Usage

clusterRank(
  graph,
  vids = V(graph),
  directed = FALSE,
  loops = TRUE,
  ncores = "default",
  verbose = FALSE
)

Value

A numeric vector contaning the ClusterRank centrality scores for the selected vertices.

Arguments

graph

The input graph as igraph object

vids

Vertex sequence, the vertices for which the centrality values are returned. Default is all vertices.

directed

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

loops

Logical; whether the loop edges are also counted.

ncores

Integer; the number of cores to be used for parallel processing. If ncores == "default" (default), the number of cores to be used will be the max(number of available cores) - 1. We recommend leaving ncores argument as is (ncores = "default").

verbose

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

See Also

ivi, cent_network.vis

Other centrality functions: betweenness(), collective.influence(), h_index(), lh_index(), neighborhood.connectivity(), sirir()

Examples

if (FALSE) {
MyData <- coexpression.data
My_graph <- graph_from_data_frame(MyData)
GraphVertices <- V(My_graph)
cr <- clusterRank(graph = My_graph, vids = GraphVertices, 
directed = FALSE, loops = TRUE, ncores = 1)
}