comm.eigen: Community Eigenvector Centrality

In AlexChristensen/NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis

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 <[e-mail]>

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)

AlexChristensen/NetworkToolbox documentation built on March 6, 2023, 5:08 p.m.