closeness.residual: Find the residual closeness centrality
In centiserve: Find Graph Centrality Indices

Description Usage Arguments Details Value Author(s) References Examples

Residual closeness centrality defined as:

C_i=sum(1/2^d(i,j), j!=i)

1 2	closeness.residual(graph, vids = V(graph), mode = c("all", "out", "in"), weights = NULL)

`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 constant, gives whether the shortest paths to or from the given vertices should be calculated for directed graphs. If out then the shortest paths from the vertex, if in then to it will be considered. If all, the default, then the corresponding undirected graph will be used, ie. not directed paths are searched. 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).

This function calculate closeness of a vertex as Dangalchev defination.
More detail at Residual Closeness Centrality

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

Mahdi Jalili m_jalili@farabi.tums.ac.ir

Dangalchev, Chavdar. "Residual closeness in networks." Physica A: Statistical Mechanics and its Applications 365.2 (2006): 556-564.

1 2	g <- graph(c(1,2,2,3,3,4,4,2)) closeness.residual(g)

Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

Loading required package: Matrix
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