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:
Have high degree/strength
Have high betweenness centrality
Have low clustering coefficient
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.
hubness(g, xfm.type = g$xfm.type, weights = NULL, prop.keep = 0.2)A numeric vector with the vertices' hubness score
An igraph graph object
Character string specifying how to transform the weights.
Default: 1/w
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.
Numeric (between 0 and 1) indicating the proportion of vertices to consider as having a high score. Default: 0.2 (20%)
Christopher G. Watson, cgwatson@bu.edu
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. tools:::Rd_expr_doi("10.1523/JNEUROSCI.2874-10.2010")