betweenness: Vertex betweenness centrality

In influential: Identification and Classification of the Most Influential Nodes

Vertex betweenness centrality

Description

This function and all of its descriptions have been obtained from the igraph package.

Usage

betweenness(
  graph,
  v = V(graph),
  directed = TRUE,
  weights = NULL,
  normalized = FALSE,
  ...
)

Arguments

graph	The graph to analyze (an igraph graph).
v	The vertices for which the vertex betweenness will be calculated.
directed	Logical, whether directed paths should be considered while determining the shortest paths.
weights	Optional positive weight vector for calculating weighted betweenness. If the graph has a weight edge attribute, then this is used by default. Weights are used to calculate weighted shortest paths, so they are interpreted as distances.
normalized	Logical scalar, whether to normalize the betweenness scores. If TRUE, then the results are normalized.
...	Additional arguments according to the original betweenness function in the package igraph.

Value

A numeric vector with the betweenness score for each vertex in v.

See Also

ivi, cent_network.vis, and betweenness for a complete description on this function

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

Examples

## Not run: 
MyData <- coexpression.data
My_graph <- graph_from_data_frame(MyData)
GraphVertices <- V(My_graph)
My_graph_betweenness <- betweenness(My_graph, v = GraphVertices,
                        directed = FALSE, normalized = FALSE)
                        
## End(Not run)

influential documentation built on Nov. 19, 2023, 9:06 a.m.