graphcommunities: Community detection using edge betweenness

In dkoban/SNAToolKit:

Description

Function that identifies structurally-based groups using the Girvan<e2><80><93>Newman, edge betweenness community detection algorithm and then produces a plot where nodes are colored based on group membership.

Usage

graph_communities(edgelist, sizeby = "none", weighted = FALSE)

Arguments

sizeby	custom option to size nodes based on either "degree", "eigenvector", "betweenness", or "closeness" centrality. Default to "none".
weighted	custom option to size edges based on weighted values. Default to "FALSE".
edglist	a dataframe that contains a list of people and their associates. The first column represents source nodes and the second column represents target nodes.

Examples

# minimal example
source <- c("a", "a", "b", "c", "d", "d", "d")
target <- c("b", "c", "c", "d", "e", "f", "g")
associations <- data.frame(source, target)
graph_communities(edgelist = associations, sizeby = "betweenness")

# FM 3-24 example affiliation matrix
data("fm3_24_affiliation")
affiliations <- as.data.frame(fm3_24_affiliation)
associations <- transform2social(edgelist = affiliations)
graph_communities(edgelist = associations, sizeby = "betweenness", weighted = TRUE)

dkoban/SNAToolKit documentation built on May 20, 2019, 1:28 p.m.