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Closeness centrality of vertices

Source: R/centrality.R
closeness.Rd

Closeness centrality measures how many steps is required to access every other vertex from a given vertex.

Usage

closeness(
  graph,
  vids = V(graph),
  mode = c("out", "in", "all", "total"),
  weights = NULL,
  normalized = FALSE,
  cutoff = -1
)

Arguments

graph

The graph to analyze.

vids

The vertices for which closeness will be calculated.

mode

Character string, defined the types of the paths used for measuring the distance in directed graphs. “in” measures the paths to a vertex, “out” measures paths from a vertex, all uses undirected paths. This argument is ignored for undirected graphs.

weights

Optional positive weight vector for calculating weighted closeness. If the graph has a weight edge attribute, then this is used by default. Weights are used for calculating weighted shortest paths, so they are interpreted as distances.

normalized

Logical scalar, whether to calculate the normalized closeness, i.e. the inverse average distance to all reachable vertices. The non-normalized closeness is the inverse of the sum of distances to all reachable vertices.

cutoff

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

Value

Numeric vector with the closeness values of all the vertices in v.

Details

The closeness centrality of a vertex is defined as the inverse of the sum of distances to all the other vertices in the graph:

$$\frac{1}{\sum_{i\ne v} d_{vi}}$$

If there is no (directed) path between vertex v and i, then i is omitted from the calculation. If no other vertices are reachable from v, then its closeness is returned as NaN.

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 (which is the default), then the function calculates the exact closeness scores. Since igraph 1.6.0, a cutoff value of zero is treated literally, i.e. path with a length greater than zero are ignored.

Closeness centrality is meaningful only for connected graphs. In disconnected graphs, consider using the harmonic centrality with harmonic_centrality()

References

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

See also

Centrality measures alpha_centrality(), betweenness(), diversity(), eigen_centrality(), harmonic_centrality(), hub_score(), page_rank(), power_centrality(), spectrum(), strength(), subgraph_centrality()

Author

Gabor Csardi csardi.gabor@gmail.com

Examples


g <- make_ring(10)
g2 <- make_star(10)
closeness(g)
#>  [1] 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
closeness(g2, mode = "in")
#>  [1] 0.1111111       NaN       NaN       NaN       NaN       NaN       NaN
#>  [8]       NaN       NaN       NaN
closeness(g2, mode = "out")
#>  [1] NaN   1   1   1   1   1   1   1   1   1
closeness(g2, mode = "all")
#>  [1] 0.11111111 0.05882353 0.05882353 0.05882353 0.05882353 0.05882353
#>  [7] 0.05882353 0.05882353 0.05882353 0.05882353