The vertex and edge betweenness are (roughly) defined by the number of geodesics (shortest paths) going through a vertex or an edge.

## Usage

```
estimate_betweenness(
graph,
vids = V(graph),
directed = TRUE,
cutoff,
weights = NULL,
nobigint = TRUE
)
betweenness(
graph,
v = V(graph),
directed = TRUE,
weights = NULL,
nobigint = TRUE,
normalized = FALSE,
cutoff = -1
)
edge_betweenness(
graph,
e = E(graph),
directed = TRUE,
weights = NULL,
cutoff = -1
)
```

## Arguments

- graph
The graph to analyze.

- vids
The vertices for which the vertex betweenness estimation will be calculated.

- directed
Logical, whether directed paths should be considered while determining the shortest paths.

- cutoff
The maximum path length to consider when calculating the betweenness. If zero or negative then there is no such limit.

- 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.- nobigint
Logical scalar, whether to use big integers during the calculation. Deprecated since igraph 1.3 and will be removed in igraph 1.4.

- v
The vertices for which the vertex betweenness will be calculated.

- normalized
Logical scalar, whether to normalize the betweenness scores. If

`TRUE`

, then the results are normalized by the number of ordered or unordered vertex pairs in directed and undirected graphs, respectively. In an undirected graph, $$B^n=\frac{2B}{(n-1)(n-2)},$$ where \(B^n\) is the normalized, \(B\) the raw betweenness, and \(n\) is the number of vertices in the graph.- e
The edges for which the edge betweenness will be calculated.

## Value

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

for `betweenness()`

.

A numeric vector with the edge betweenness score for each edge in `e`

for `edge_betweenness()`

.

## Details

The vertex betweenness of vertex `v`

is defined by

$$\sum_{i\ne j, i\ne v, j\ne v} g_{ivj}/g_{ij}$$

The edge betweenness of edge `e`

is defined by

$$\sum_{i\ne j} g_{iej}/g_{ij}.$$

`betweenness()`

calculates vertex betweenness, `edge_betweenness()`

calculates edge betweenness.

Here \(g_{ij}\) is the total number of shortest paths between vertices \(i\) and \(j\) while \(g_{ivj}\) is the number of those shortest paths which pass though vertex \(v\).

Both functions allow you to consider only paths of length `cutoff`

or
smaller; this can be run for larger graphs, as the running time is not
quadratic (if `cutoff`

is small). If `cutoff`

is zero or negative,
then the function calculates the exact betweenness scores. Using zero as a
cutoff is *deprecated* and future versions (from 1.4.0) will treat zero
cutoff literally (i.e. no paths considered at all). If you want no cutoff,
use a negative number.

`estimate_betweenness()`

and `estimate_edge_betweenness()`

are
aliases for `betweenness()`

and `edge_betweenness()`

, with a different
argument order, for sake of compatibility with older versions of igraph.

For calculating the betweenness a similar algorithm to the one proposed by Brandes (see References) is used.

## References

Freeman, L.C. (1979). Centrality in Social Networks I:
Conceptual Clarification. *Social Networks*, 1, 215-239.

Ulrik Brandes, A Faster Algorithm for Betweenness Centrality. *Journal
of Mathematical Sociology* 25(2):163-177, 2001.

## See also

`closeness()`

, `degree()`

, `harmonic_centrality()`

Centrality measures
`alpha_centrality()`

,
`closeness()`

,
`diversity()`

,
`eigen_centrality()`

,
`harmonic_centrality()`

,
`hub_score()`

,
`page_rank()`

,
`power_centrality()`

,
`spectrum()`

,
`strength()`

,
`subgraph_centrality()`

Centrality measures
`alpha_centrality()`

,
`closeness()`

,
`diversity()`

,
`eigen_centrality()`

,
`harmonic_centrality()`

,
`hub_score()`

,
`page_rank()`

,
`power_centrality()`

,
`spectrum()`

,
`strength()`

,
`subgraph_centrality()`

Centrality measures
`alpha_centrality()`

,
`closeness()`

,
`diversity()`

,
`eigen_centrality()`

,
`harmonic_centrality()`

,
`hub_score()`

,
`page_rank()`

,
`power_centrality()`

,
`spectrum()`

,
`strength()`

,
`subgraph_centrality()`

## Author

Gabor Csardi csardi.gabor@gmail.com

## Examples

```
g <- sample_gnp(10, 3 / 10)
betweenness(g)
#> [1] 5.166667 0.000000 1.750000 11.666667 9.750000 5.500000 2.583333
#> [8] 8.000000 0.000000 5.583333
edge_betweenness(g)
#> [1] 3.083333 9.166667 9.000000 5.416667 6.916667 7.250000 10.416667
#> [8] 7.666667 6.916667 9.000000 7.083333 4.000000 9.083333
```