graph.communities: Graph Communities

In rlebron-bioinfo/gnlearn: Genetic Network Learning

Description

This function allows you to detect how many communities are in the graph and to which community each node and edge belongs.

Usage

graph.communities(
  x,
  algorithm = c("louvain", "edge.betweenness", "fast.greedy", "label.prop",
    "leading.eigen", "optimal", "spinglass", "walktrap"),
  network = TRUE,
  network.layout = c("circle", "star", "grid", "sphere", "nicely"),
  interactive.network = FALSE,
  network.isolated = TRUE,
  dendrogram = FALSE,
  dendrogram.type = c("fan", "phylogram", "cladogram", "unrooted", "radial"),
  from = c("auto", "adjacency", "edges", "graph", "igraph", "bnlearn")
)

Arguments

x	Graph object.
algorithm	Algorithm for finding communities: 'louvain', 'edge.betweenness', 'fast.greedy', 'label.prop', 'leading.eigen', 'optimal', 'spinglass', or 'walktrap'. Default: 'louvain'
network	Whether or not to plot the network. Default: TRUE
network.layout	igraph network layout (optional): 'grid', 'star', 'circle', 'sphere', or 'nicely'. Default: 'circle'
interactive.network	Interactive network (optional). Default: FALSE
network.isolated	Whether or not to include isolated nodes in the plot (optional). Default: TRUE
dendrogram	Whether or not to plot a dendrogram (when possible). Default: FALSE
dendrogram.type	Type of phylogeny to be drawn: 'fan', 'phylogram', 'cladogram', 'unrooted', or 'radial'. Default: 'fan'
from	Input format (optional).

Examples

communities <- graph.communities(g)
communities <- graph.communities(g, algorithm='louvain', network=TRUE, network.isolated=FALSE, dendrogram=FALSE)
communities <- graph.communities(g, algorithm='walktrap', network=FALSE, dendrogram=TRUE, dendrogram.type='cladogram')

rlebron-bioinfo/gnlearn documentation built on July 25, 2020, 12:38 p.m.