NetworkToolbox: comm.eigen – R documentation

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comm.eigen

Community Eigenvector Centrality

Description

Computes the flow.frac for each community in the network. The values are equivalent to the community's eigenvector centrality

Usage

comm.eigen(A, comm, weighted = TRUE)

Arguments

`A`	An adjacency matrix
`comm`	A vector or matrix corresponding to the community each node belongs to
`weighted`	Is the network weighted? Defaults to `TRUE`. Set to `FALSE` for weighted measures

Value

A vector of community eigenvector centrality values for each specified community in the network (larger values suggest more central positioning)

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Giscard, P. L., & Wilson, R. C. (2018). A centrality measure for cycles and subgraphs II. Applied Network Science, 3, 9.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

comm <- igraph::walktrap.community(convert2igraph(abs(A)))$membership

result <- comm.eigen(A, comm)

NetworkToolbox

Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis

v1.4.1

GPL (>= 3.0)

Authors

Alexander Christensen [aut, cre] (<https://orcid.org/0000-0002-9798-7037>), Guido Previde Massara [ctb] (<https://orcid.org/0000-0003-0502-2789>)

Initial release

2020-12-07