met.eigen {ANTs}R Documentation

Eigenvector Centrality

Description

Calculates the node metric met.evcent centrality for all vertices.

Usage

met.eigen(M, df = NULL, dfid = NULL, sym = TRUE, binary = FALSE, out = FALSE)

Arguments

M

a square adjacency matrix, or a list of square adjacency matrices, or an output of ANT functions stat.ds.grp, stat.df.focal, stat.net.lk.

df

a data frame of same length as the input matrix or a list of data frames if argument M is a list of matrices or an output of ANT functions stat.ds.grp, stat.df.focal, stat.net.lk.

dfid

an integer or a string indicating the column with individual ids in argument df.

sym

if TRUE, then it symmetrizes the matrix. Otherwise, it calculates geodesic distances and diameter according to the directionality of the links.

binary

a boolean, if TRUE, it calculates the binary version of the eigenvector centrality.

out

if TRUE, it considers outgoing ties to compute the shortest paths.

Details

Eigenvector centrality is the first non-negative met.evcent value obtained through the linear transformation of an adjacency matrix. This centrality measure quantifies not only a node connectedness, but also the connections of the nodes to whom it is connected. Thus, a node can have a high met.evcent value by having a high met.degree or met.strength, or by being connected to nodes that have high degrees or strengths.

Value

Author(s)

Sebastian Sosa, Ivan Puga-Gonzalez.

References

Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of mathematical sociology, 2(1), 113-120.

Sosa, S. (2018). Social Network Analysis, in: Encyclopedia of Animal Cognition and Behavior. Springer.

Examples

met.eigen(sim.m)
head(sim.df)
met.eigen(sim.m,df=sim.df)

[Package ANTs version 0.0.16 Index]