participation: Participation Coefficient

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

Participation Coefficient

Description

Computes the participation coefficient for each node. The participation coefficient measures the strength of a node's connections within its community. Positive and negative signed weights for participation coefficients are computed separately.

Usage

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

Value

Returns a list containing:

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

Author(s)

Alexander Christensen <[e-mail]>

References

Guimera, R., & Amaral, L. A. N. (2005). Functional cartography of complex metabolic networks. Nature, 433, 895-900.

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

Examples

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

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

pc <- participation(A, comm = comm)

# Walktrap factors
wpc <- participation(A, comm = "walktrap")

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