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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/assortativity.R
\name{assortativity}
\alias{assortativity}
\alias{assortativity_nominal}
\alias{assortativity_degree}
\title{Assortativity coefficient}
\usage{
assortativity(
graph,
values,
...,
values.in = NULL,
directed = TRUE,
normalized = TRUE,
types1 = NULL,
types2 = NULL
)
assortativity_nominal(graph, types, directed = TRUE, normalized = TRUE)
assortativity_degree(graph, directed = TRUE)
}
\arguments{
\item{graph}{The input graph, it can be directed or undirected.}
\item{values}{The vertex values, these can be arbitrary numeric values.}
\item{...}{These dots are for future extensions and must be empty.}
\item{values.in}{A second value vector to use for the incoming edges when
calculating assortativity for a directed graph.
Supply \code{NULL} here if
you want to use the same values for outgoing and incoming edges.
This
argument is ignored (with a warning) if it is not \code{NULL} and undirected
assortativity coefficient is being calculated.}
\item{directed}{Logical scalar, whether to consider edge directions for
directed graphs.
This argument is ignored for undirected graphs.
Supply
\code{TRUE} here to do the natural thing, i.e. use directed version of the
measure for directed graphs and the undirected version for undirected
graphs.}
\item{normalized}{Boolean, whether to compute the normalized assortativity.
The non-normalized nominal assortativity is identical to modularity.
The non-normalized value-based assortativity is simply the covariance of the
values at the two ends of edges.}
\item{types1, types2}{\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}
Deprecated aliases for \code{values} and \code{values.in}, respectively.}
\item{types}{Vector giving the vertex types. They as assumed to be integer
numbers, starting with one. Non-integer values are converted to integers
with \code{\link[=as.integer]{as.integer()}}.}
}
\value{
A single real number.
}
\description{
The assortativity coefficient is positive if similar vertices (based on some
external property) tend to connect to each, and negative otherwise.
}
\details{
The assortativity coefficient measures the level of homophyly of the graph,
based on some vertex labeling or values assigned to vertices. If the
coefficient is high, that means that connected vertices tend to have the
same labels or similar assigned values.
M.E.J. Newman defined two kinds of assortativity coefficients, the first one
is for categorical labels of vertices. \code{assortativity_nominal()}
calculates this measure. It is defined as
\deqn{r=\frac{\sum_i e_{ii}-\sum_i a_i b_i}{1-\sum_i a_i b_i}}{
r=(sum(e(i,i), i) - sum(a(i)b(i), i)) / (1 - sum(a(i)b(i), i))}
where \eqn{e_{ij}}{e(i,j)} is the fraction of edges connecting vertices of
type \eqn{i} and \eqn{j}, \eqn{a_i=\sum_j e_{ij}}{a(i)=sum(e(i,j), j)} and
\eqn{b_j=\sum_i e_{ij}}{b(j)=sum(e(i,j), i)}.
The second assortativity variant is based on values assigned to the
vertices. \code{assortativity()} calculates this measure. It is defined as
\deqn{r=\frac1{\sigma_q^2}\sum_{jk} jk(e_{jk}-q_j q_k)}{
sum(jk(e(j,k)-q(j)q(k)), j, k) / sigma(q)^2}
for undirected graphs (\eqn{q_i=\sum_j e_{ij}}{q(i)=sum(e(i,j), j)}) and as
\deqn{r=\frac1{\sigma_o\sigma_i}\sum_{jk}jk(e_{jk}-q_j^o q_k^i)}{
sum(jk(e(j,k)-qout(j)qin(k)), j, k) / sigma(qin) / sigma(qout) }
for directed ones. Here \eqn{q_i^o=\sum_j e_{ij}}{qout(i)=sum(e(i,j), j)},
\eqn{q_i^i=\sum_j e_{ji}}{qin(i)=sum(e(j,i), j)}, moreover,
\eqn{\sigma_q}{\sigma(q)}, \eqn{\sigma_o}{\sigma(qout)} and
\eqn{\sigma_i}{\sigma(qin)} are the standard deviations of \eqn{q},
\eqn{q^o}{qout} and \eqn{q^i}{qin}, respectively.
The reason of the difference is that in directed networks the relationship
is not symmetric, so it is possible to assign different values to the
outgoing and the incoming end of the edges.
\code{assortativity_degree()} uses vertex degree as vertex values
and calls \code{assortativity()}.
Undirected graphs are effectively treated as directed ones with all-reciprocal edges.
Thus, self-loops are taken into account twice in undirected graphs.
}
\examples{
# random network, close to zero
assortativity_degree(sample_gnp(10000, 3 / 10000))
# BA model, tends to be dissortative
assortativity_degree(sample_pa(10000, m = 4))
}
\references{
M. E. J. Newman: Mixing patterns in networks, \emph{Phys. Rev.
E} 67, 026126 (2003) \url{https://arxiv.org/abs/cond-mat/0209450}
M. E. J. Newman: Assortative mixing in networks, \emph{Phys. Rev. Lett.} 89,
208701 (2002) \url{https://arxiv.org/abs/cond-mat/0205405}
}
\author{
Gabor Csardi \email{csardi.gabor@gmail.com}
}
\keyword{graphs}
\section{Related documentation in the C library}{\href{https://igraph.org/c/html/latest/igraph-Structural.html#igraph_assortativity}{\code{igraph_assortativity()}}, \href{https://igraph.org/c/html/latest/igraph-Structural.html#igraph_assortativity_nominal}{\code{igraph_assortativity_nominal()}}, \href{https://igraph.org/c/html/latest/igraph-Structural.html#igraph_assortativity_degree}{\code{igraph_assortativity_degree()}}.}
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