Eigen vector centrality associates with each node \(v\)
the positive value \(e(v)\), such that:
\( \sum_{e}^v w(uv) * e(u) = \lambda * e(v) \). Thus,
\(e(v)\) is the Perron-Frobenius eigenvector of the adjacency matrix of the
tree.
Usage
eigen_centrality(phy, weight = TRUE, scale = FALSE, use_rspectra = FALSE)
Arguments
- phy
phylo object or ltable
- weight
if TRUE, uses branch lengths.
- scale
if TRUE, the Eigenvector is rescaled
- use_rspectra
boolean to indicate whether the helping package RSpectra
should be used, which is faster, but returns fewer eigen values.
Value
List with the Eigen vector and the leading Eigen value
References
Chindelevitch, Leonid, et al. "Network science inspires novel
tree shape statistics." Plos one 16.12 (2021): e0259877.