robin (version 2.0.0)

robinRobust: robinRobust

Description

This functions implements a procedure to examine the stability of the partition recovered by some algorithm against random perturbations of the original graph structure.

Usage

robinRobust(
  graph,
  graphRandom,
  method = c("walktrap", "edgeBetweenness", "fastGreedy", "louvain", "spinglass",
    "leadingEigen", "labelProp", "infomap", "optimal", "leiden", "other"),
  ...,
  FUN = NULL,
  measure = c("vi", "nmi", "split.join", "adjusted.rand"),
  type = "independent",
  verbose = TRUE,
  dist = "Other",
  BPPARAM = BiocParallel::bpparam()
)

Value

A list object with two matrices: - the matrix "Mean" with the means of the procedure for the graph - the matrix "MeanRandom" with the means of the procedure for the random graph.

Arguments

graph

The output of prepGraph.

graphRandom

The output of random function.

method

The clustering method, one of "walktrap", "edgeBetweenness", "fastGreedy", "louvain", "spinglass", "leadingEigen", "labelProp", "infomap", "leiden","optimal".

...

other parameter.

FUN

in case the @method parameter is "other" there is the possibility to use a personal function passing its name through this parameter. The personal parameter has to take as input the @graph and the @weights (that can be NULL), and has to return a community object.

measure

The stability measure, one of "vi", "nmi", "split.join", "adjusted.rand" all normalized and used as distances. "nmi" refers to 1- nmi and "adjusted.ran" refers to 1-adjusted.rand.

type

The type of robin construction, dependent or independent.

verbose

flag for verbose output (default as TRUE).

dist

Option to rewire in a manner that retains overall graph weight regardless of distribution of edge weights. This option is invoked by putting any text into this field. Defaults to "Other". See rewireR for details.

BPPARAM

the BiocParallel object of class bpparamClass that specifies the back-end to be used for computations. See bpparam for details.

Examples

my_file <- system.file("example/football.gml", package="robin")
graph <- prepGraph(file=my_file, file.format="gml")
graphRandom <- random(graph=graph)
robinRobust(graph=graph, graphRandom=graphRandom, method="leiden")