# betweenness: Vertex betweenness centrality In influential: Identification and Classification of the Most Influential Nodes

 betweenness R Documentation

## 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.

`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.