NetworkToolbox (version 1.4.2)

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)

References

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

Examples

# NOT RUN {
# 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)

# }