centiserve (version 1.0.0)

hubbell: 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.

Value

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

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

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

# NOT RUN {
g <- barabasi.game(100)
hubbell(g)
# }