clusterRank: ClusterRank (CR)

In influential: Identification and Classification of the Most Influential Nodes

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
)

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).

Value

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

See Also

ivi, cent_network.vis

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

Examples

## Not run: 
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)

## End(Not run)

influential documentation built on May 29, 2024, 11:49 a.m.