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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/centrality.R
\name{spectrum}
\alias{spectrum}
\alias{igraph.eigen.default}
\title{Eigenvalues and eigenvectors of the adjacency matrix of a graph}
\usage{
spectrum(
graph,
algorithm = c("arpack", "auto", "lapack", "comp_auto", "comp_lapack", "comp_arpack"),
which = list(),
options = arpack_defaults()
)
}
\arguments{
\item{graph}{The input graph, can be directed or undirected.}
\item{algorithm}{The algorithm to use. Currently only \code{arpack} is
implemented, which uses the ARPACK solver. See also \code{\link[=arpack]{arpack()}}.}
\item{which}{A list to specify which eigenvalues and eigenvectors to
calculate. By default the leading (i.e. largest magnitude) eigenvalue and
the corresponding eigenvector is calculated.}
\item{options}{Options for the ARPACK solver. See
\code{\link[=arpack_defaults]{arpack_defaults()}}.}
}
\value{
Depends on the algorithm used.
For \code{arpack} a list with three entries is returned: \item{options}{See
the return value for \code{arpack()} for a complete description.}
\item{values}{Numeric vector, the eigenvalues.} \item{vectors}{Numeric
matrix, with the eigenvectors as columns.}
}
\description{
Calculate selected eigenvalues and eigenvectors of a (supposedly sparse)
graph.
}
\details{
The \code{which} argument is a list and it specifies which eigenvalues and
corresponding eigenvectors to calculate: There are eight options:
\enumerate{ \item Eigenvalues with the largest magnitude. Set \code{pos} to
\code{LM}, and \code{howmany} to the number of eigenvalues you want. \item
Eigenvalues with the smallest magnitude. Set \code{pos} to \code{SM} and
\code{howmany} to the number of eigenvalues you want. \item Largest
eigenvalues. Set \code{pos} to \code{LA} and \code{howmany} to the number of
eigenvalues you want. \item Smallest eigenvalues. Set \code{pos} to
\code{SA} and \code{howmany} to the number of eigenvalues you want. \item
Eigenvalues from both ends of the spectrum. Set \code{pos} to \code{BE} and
\code{howmany} to the number of eigenvalues you want. If \code{howmany} is
odd, then one more eigenvalue is returned from the larger end. \item
Selected eigenvalues. This is not (yet) implemented currently. \item
Eigenvalues in an interval. This is not (yet) implemented. \item All
eigenvalues. This is not implemented yet. The standard \code{eigen} function
does a better job at this, anyway. }
Note that ARPACK might be unstable for graphs with multiple components, e.g.
graphs with isolate vertices.
}
\examples{
## Small example graph, leading eigenvector by default
kite <- make_graph("Krackhardt_kite")
spectrum(kite)[c("values", "vectors")]
## Double check
eigen(as_adjacency_matrix(kite, sparse = FALSE))$vectors[, 1]
## Should be the same as 'eigen_centrality' (but rescaled)
cor(eigen_centrality(kite)$vector, spectrum(kite)$vectors)
## Smallest eigenvalues
spectrum(kite, which = list(pos = "SM", howmany = 2))$values
}
\seealso{
\code{\link[=as_adjacency_matrix]{as_adjacency_matrix()}} to create a (sparse) adjacency matrix.
Centrality measures
\code{\link{alpha_centrality}()},
\code{\link{authority_score}()},
\code{\link{betweenness}()},
\code{\link{closeness}()},
\code{\link{diversity}()},
\code{\link{eigen_centrality}()},
\code{\link{harmonic_centrality}()},
\code{\link{hits_scores}()},
\code{\link{page_rank}()},
\code{\link{power_centrality}()},
\code{\link{strength}()},
\code{\link{subgraph_centrality}()}
}
\author{
Gabor Csardi \email{csardi.gabor@gmail.com}
}
\concept{centrality}
\keyword{graphs}
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