met.eigen {ANTs} | R Documentation |
Calculates the node metric met.evcent centrality for all vertices.
met.eigen(M, df = NULL, dfid = NULL, sym = TRUE, binary = FALSE, out = FALSE)
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. |
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.
An integer vector of nodes eigenvector centrality if argument df is NULL.
A list of integer vectors of nodes eigenvector centrality if argument M is a list of matrices and if argument df is NULL.
A list of arguments df with a new column for nodes eigenvector centrality if argumentdf is not NULL. The name of the column is adapted according to arguments value binary, sym and out.
A list of arguments df with a new column for nodes eigenvector centrality if 1) argument df is not NULL, 2) argument M is an output from ANT functions stat.ds.grp, stat.df.focal, stat.net.lk for multiple matrices permutations, and 3) argument df is a list of data frames of same length as argument M.The name of the column of each element of the list is adapted according to argument value binary.
Sebastian Sosa, Ivan Puga-Gonzalez.
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.
met.eigen(sim.m)
head(sim.df)
met.eigen(sim.m,df=sim.df)