hubness {brainGraph}

Calculate vertex hubness

Description

hubness calculates the “hubness” (see reference) of the vertices in a graph. These are vertices which meet at least two of the following four criteria:

1. Have high degree/strength

2. Have high betweenness centrality

3. Have low clustering coefficient

4. Have low average path length

For each criterion, “high” or “low” means “in the top 20%” across all vertices. Vertices meeting any of the criteria get a value of 1 for that metric; these are summed to yield the hubness score which ranges from 0-4. As in the reference article, vertices with a score of 2 or higher are to be considered hubs, although that determination isn't made in this function.

Usage

hubness(g, xfm.type = g$xfm.type, weights = NULL, prop.keep = 0.2)

Arguments

g

An igraph graph object

xfm.type

Character string specifying how to transform the weights. Default: 1/w

weights

Numeric vector of edge weights; if NULL (the default), and if the graph has edge attribute weight, then that will be used. To avoid using weights, this should be NA.

prop.keep

Numeric (between 0 and 1) indicating the proportion of vertices to consider as having a high score. Default: 0.2 (20%)

Value

A numeric vector with the vertices' hubness score

Author(s)

Christopher G. Watson, [e-mail]

References

van den Heuvel, M.P. and Mandl, R.C.W. and Stam, C.J. and Kahn, R.S. and Pol, H.E.H. (2010) Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis. The Journal of Neuroscience, 30(47), 15915–15926. doi: 10.1523/JNEUROSCI.2874-10.2010