bonpow()
was renamed to power_centrality()
to create a more
consistent API.
Usage
bonpow(
graph,
nodes = V(graph),
loops = FALSE,
exponent = 1,
rescale = FALSE,
tol = 1e-07,
sparse = TRUE
)
Arguments
- graph
the input graph.
- nodes
vertex sequence indicating which vertices are to be included in the calculation. By default, all vertices are included.
- loops
boolean indicating whether or not the diagonal should be treated as valid data. Set this true if and only if the data can contain loops.
loops
isFALSE
by default.- exponent
exponent (decay rate) for the Bonacich power centrality score; can be negative
- rescale
if true, centrality scores are rescaled such that they sum to 1.
- tol
tolerance for near-singularities during matrix inversion (see
solve()
)- sparse
Logical scalar, whether to use sparse matrices for the calculation. The ‘Matrix’ package is required for sparse matrix support