hubbell {centiserve}R Documentation

Find the Hubbell centrality or the Hubbell Index

Description

Hubbell centrality defined as:

C(h) = E + WC(h)

where E is some exogeneous input and w is a weight matrix derived from the adjancancy matrix A.

Usage

hubbell(graph, vids = V(graph), weights = NULL, weightfactor = 0.5)

Arguments

graph

The input graph as igraph object

vids

Vertex sequence, the vertices for which the centrality values are returned. Default is all vertices.

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).

weightfactor

The weight factorLogical which must be greater than 0. The defualt is 0.5.

Details

This centrality value is defined by means of a weighted and loop allowed network. The weighted adjacency matrix w of a network G is asymmetric and contains real-valued weights for each edge.
More detail at Hubbell Index

Value

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

Author(s)

Mahdi Jalili m_jalili@farabi.tums.ac.ir

Algorithm adapted from CentiLib (Grabler, Johannes, 2012).

References

Hubbell, Charles H. "An input-output approach to clique identification." Sociometry (1965): 377-399.

Grabler, Johannes, Dirk Koschutzki, and Falk Schreiber. "CentiLib: comprehensive analysis and exploration of network centralities." Bioinformatics 28.8 (2012): 1178-1179.

Examples

g <- barabasi.game(100)
hubbell(g)

[Package centiserve version 1.0.0 Index]