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


  v = V(graph),
  directed = TRUE,
  weights = NULL,
  normalized = FALSE,
  cutoff = -1

  e = E(graph),
  directed = TRUE,
  weights = NULL,
  cutoff = -1



The graph to analyze.


The vertices for which the vertex betweenness will be calculated.


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


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.


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.


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


The edges for which the edge betweenness will be calculated.


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().


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 negative (the default), then the function calculates the exact betweenness scores. Since igraph 1.6.0, a cutoff value of zero is treated literally, i.e. paths of length larger than zero are ignored.

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


edge_betweenness() might give false values for graphs with multiple edges.


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.


Gabor Csardi


g <- sample_gnp(10, 3 / 10)
#>  [1]  0  0  0 14  0  6  6  0  0  0
#>  [1]  4  2  7  8 12  1  4  2  7  7