closeness.freeman: Find the closeness centrality in a strongly connected graph

In centiserve: Find Graph Centrality Indices

Description

Freeman closeness centrality defined as:

1/sum( d(v,i), i != v)

Usage

closeness.freeman(graph, vids = V(graph), mode = c("all", "out", "in"),
  weights = NULL, normalized = FALSE)

Arguments

graph	The input graph as igraph object
vids	Vertex sequence, the vertices for which the centrality values are returned. Default is all vertices.
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	Possibly a numeric vector giving edge weights. If this is NULL, the default, and the graph has a weight edge attribute, then the attribute is used. If this is NA then no weights are used (even if the graph has a weight attribute).
normalized	Logical scalar, whether to calculate the normalized score.

Details

Because closeness is infinite if there is no path between two vertex so freeman closeness require a strongly connected graph. In igraph if there is no (directed) path between vertex v and i then the total number of vertices is used in the formula instead of the path length.
More detail at Closeness Centrality

Value

A numeric vector contaning the centrality scores for the selected vertices.

Author(s)

Mahdi Jalili [e-mail]

Use igraph package closeness function.

References

Freeman, Linton C. "Centrality in social networks conceptual clarification." Social networks 1.3 (1979): 215-239.

Examples

g <- graph(c(1,2,2,3,3,4,4,2), directed=FALSE)
closeness.freeman(g)

centiserve documentation built on May 2, 2019, 6:01 a.m.