eigen_centr_mult: Calculate eigenvector centrality for multiple graphs.

In abnormally-distributed/rsfcNet: Analyze resting state functional connectivity networks

Description

This function calculates the eigenvector centrality for multiple graphs.

Usage

eigen_centr_mult(
  graphs,
  normalize = c(0, 1, 2, 3, 4, 5),
  col.names = NULL,
  row.names = NULL
)

Arguments

graphs	a list of igraph objects.
normalize	how the normalization of eigenvector centrality should be treated. The options are as follows: If 0, no normalization is applied. If 1, the absolute value of the principal eigenvector is used as in the MATLAB Brain Connectivity Toolbox (Rubinov & Sporns, 2010), but no normalization is applied. If 2, the principal eigenvector is taken as an absolute value, and the result is divided by the maximum value such that the largest eigenvector centrality is 1. If 3, the ranks of the raw eigenvector are taken, and the ranks divided by the maximum rank such that the largest eigenvector centrality is 1. If 4, eigenvector entries less than zero are set to zero, and then the scores are divided by the maximum value such that the largest eigenvector centrality is 1. If 5, the raw ranks of the principal eigenvector entries are returned.
col.names	The names of each column (node labels).
row.names	The names of each row (subject).

Details

The eigenvector centrality is the eigenvector for each node corresponding to the largest eigenvalue of the matrix.

Eigenvector centrality differs from degree or strength. A node with many connections does not necessarily have a high eigenvector centrality. For example, a node may have many very weak connections that yield a large value for strength/degree. Likewise, a node with high eigenvector centrality may have few connections but be well connected to a small number of important nodes.

Value

A vector of centrality scores for each node.

A matrix of the eigenvector centralities of each node for each subject.

Author(s)

Brandon Vaughan

References

Brain Connectivity Toolbox

Lohmann, G., Margulies, D. S., Horstmann, A., Pleger, B., Lepsien, J., Goldhahn, D., … Turner, R. (2010). Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain. PLoS ONE, 5(4), e10232. doi:10.1371/journal.pone.0010232

Fornito, A., Zalesky, A., & Bullmore, E. (2016). Centrality and Hubs. Chapter 5. Fundamentals of Brain Network Analysis, 137-161. doi:10.1016/b978-0-12-407908-3.00005-4

Rubinov, M. , Sporns, O. (2010) Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52:1059-69.

See Also

eigen_centr leverage_centr_mult

Examples

eigencent = eigen_centr_mult(graphs,row.names = subj_numbers,col.names = node_labels)

abnormally-distributed/rsfcNet documentation built on March 8, 2020, 5:32 p.m.