diversity: Diversity Coefficient

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

Diversity Coefficient

Description

Computes the diversity coefficient for each node. The diversity coefficient measures a node's connections to communitites outside of its own community. Nodes that have many connections to other communities will have higher diversity coefficient values. Positive and negative signed weights for diversity coefficients are computed separately.

Usage

diversity(A, comm = c("walktrap", "louvain"))

Arguments

A	Network adjacency matrix
comm	A vector of corresponding to each item's community. Defaults to "walktrap" for the cluster_walktrap community detection algorithm. Set to "louvain" for the louvain community detection algorithm. Can also be set to user-specified communities (see examples)

Details

Values closer to 1 suggest greater between-community connectivity and values closer to 0 suggest greater within-community connectivity

Value

Returns a list containing:

overall	Diversity coefficient without signs considered
positive	Diversity coefficient with only positive sign
negative	Diversity coefficient with only negative sign

Author(s)

Alexander Christensen <[e-mail]>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

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

#theoretical communities
comm <- rep(1:8, each = 6)

gdiv <- diversity(A, comm = comm)

#walktrap communities
wdiv <- diversity(A, comm = "walktrap")

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