Title: ROBustness in Network
Version: 2.0.0
Maintainer: Valeria Policastro <valeria.policastro@gmail.com>
Description: Assesses the robustness of the community structure of a network found by one or more community detection algorithm to give indications about their reliability. It detects if the community structure found by a set of algorithms is statistically significant and compares the different selected detection algorithms on the same network. robin helps to choose among different community detection algorithms the one that better fits the network of interest. Reference in Policastro V., Righelli D., Carissimo A., Cutillo L., De Feis I. (2021) https://journal.r-project.org/archive/2021/RJ-2021-040/index.html.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
URL: https://github.com/ValeriaPolicastro/robin
Depends: igraph
Imports: ggplot2, networkD3, DescTools, fdatest, methods, gridExtra, spam, qpdf, Matrix, perturbR, BiocParallel, reshape2
VignetteBuilder: knitr
Suggests: devtools, knitr, rmarkdown, testthat (≥ 2.1.0)
NeedsCompilation: no
Packaged: 2025-01-22 16:34:15 UTC; valeriapolicastro
Author: Valeria Policastro [aut, cre], Dario Righelli [aut], Luisa Cutillo [aut], Italia De Feis [aut], Annamaria Carissimo [aut]
Repository: CRAN
Date/Publication: 2025-01-22 17:30:10 UTC

createITPSplineResult

Description

creates an fdatest::ITP2 class object

Usage

createITPSplineResult(
  graph,
  model1,
  model2,
  muParam = 0,
  orderParam = 4,
  nKnots = 7,
  BParam = 10000,
  isPaired = TRUE
)

Arguments

graph

The output of prepGraph.

model1

The Mean output of the robinRobust function (or the Mean1 output of the comparison function).

model2

The MeanRandom output of the robinRobust function (or the Mean2 output of the comparison function).

muParam

the mu parameter for ITP2bspline (default 0).

orderParam

the order parameter for ITP2bspline (default 4).

nKnots

the nknots parameter for ITP2bspline (default 7).

BParam

the B parameter for ITP2bspline (default 10000).

isPaired

the paired parameter for ITP2bspline (default TRUE).

membershipCommunities

Description

This function computes the membership vector of the community structure. To detect the community structure the user can choose one of the methods implemented in igraph.

Usage

membershipCommunities(
  graph,
  method = c("walktrap", "edgeBetweenness", "fastGreedy", "louvain", "spinglass",
    "leadingEigen", "labelProp", "infomap", "optimal", "leiden", "other"),
  ...,
  FUN = NULL
)

Arguments

graph

The output of prepGraph.

method

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

...

additional parameters to use with any of the previous described methods (see igraph package community detection methods for more details i.e. cluster_walktrap)

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.

Value

Returns a numeric vector, one number for each vertex in the graph; the membership vector of the community structure.

Examples

my_file <- system.file("example/football.gml", package="robin")
graph <- prepGraph(file=my_file, file.format="gml")
membershipCommunities (graph=graph, method="louvain")

methodCommunity

Description

This function detects the community structure of a graph. To detect the community structure the user can choose one of the methods implemented in igraph.

Usage

methodCommunity(
  graph,
  method = c("walktrap", "edgeBetweenness", "fastGreedy", "louvain", "spinglass",
    "leadingEigen", "labelProp", "infomap", "optimal", "leiden", "other"),
  leiden_objective_function = c("modularity", "CPM"),
  ...,
  FUN = NULL,
  verbose = FALSE
)

Arguments

graph

The output of prepGraph.

method

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

leiden_objective_function

objective_function parameter for leiden only for method

...

additional parameters to use with any of the previous described methods (see igraph package community detection methods for more details i.e. cluster_walktrap)

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.

verbose

flag for verbose output (default as FALSE)

Value

A Communities object.

Examples

my_file <- system.file("example/football.gml", package="robin")
graph <- prepGraph(file=my_file, file.format="gml")
methodCommunity (graph=graph, method="louvain")

plot.robin

Description

This function plots two curves: the measure of the null model and the measure of the real graph or the measure of the two community detection algorithms.

Usage

## S3 method for class 'robin'
plot(x, title = "Robin plot", ...)

Arguments

x

A robin class object. The output of the functions: robinRobust and robinCompare.

title

The title for the graph. The default is "Robin plot".

...

other parameter

Value

A ggplot object.

Examples

## Not run: my_file <- system.file("example/football.gml", package="robin")
graph <- prepGraph(file=my_file, file.format="gml")
comp <- robinCompare(graph=graph, method1="fastGreedy",method2="louvain")
plot(comp)
## End(Not run)

plotComm

Description

Graphical interactive representation of the network and its communities.

Usage

plotComm(graph, members)

Arguments

graph

The output of prepGraph.

members

A membership vector of the community structure, the output of membershipCommunities.

Value

Creates an interactive plot with colorful communities, a D3 JavaScript network graph.

Examples

my_file <- system.file("example/football.gml", package="robin")
graph <- prepGraph(file=my_file, file.format="gml")
members <- membershipCommunities (graph=graph, method="louvain")
plotComm(graph, members)

plotGraph

Description

Graphical interactive representation of the network.

Usage

plotGraph(graph)

Arguments

graph

The output of prepGraph.

Value

Creates an interactive plot, a D3 JavaScript network graph.

Examples

my_file <- system.file("example/football.gml", package="robin")
graph <- prepGraph(file=my_file, file.format="gml")
plotGraph (graph)

plotMultiCompare

Description

This function plots the curves of the measure of many community detection algorithms compared.

Usage

plotMultiCompare(..., title = "Robin plot", ylim1 = FALSE)

Arguments

...

all robin objects obtained from the comparison between one community detection algorithm and all the others

title

character a title for the plot (default is "Robin plot")

ylim1

logical for spanning the y axis from 0 to 1 (default is FALSE)

Value

a ggplot2 object

Examples

my_file <- system.file("example/football.gml", package="robin")
graph <- prepGraph(file=my_file, file.format="gml")
comp1 <- robinCompare(graph=graph, method1="fastGreedy",method2="louvain")
comp2 <- robinCompare(graph=graph, method1="fastGreedy",method2="infomap")
plotMultiCompare(comp1,comp2)

prepGraph

Description

This function reads graphs from a file and prepares them for the analysis.

Usage

prepGraph(
  file,
  file.format = c("edgelist", "pajek", "ncol", "lgl", "graphml", "dimacs", "graphdb",
    "gml", "dl", "igraph"),
  numbers = FALSE,
  directed = FALSE,
  header = FALSE,
  verbose = FALSE
)

Arguments

file

The input file containing the graph.

file.format

Character constant giving the file format. Edgelist, pajek, graphml, gml, ncol, lgl, dimacs, graphdb and igraph are supported.

numbers

A logical value indicating if the names of the nodes are values.This argument is settable for the edgelist format. The default is FALSE.

directed

A logical value indicating if is a directed graph. The default is FALSE.

header

A logical value indicating whether the file contains the names of the variables as its first line.This argument is settable

verbose

flag for verbose output (default as FALSE). for the edgelist format.The default is FALSE.

Value

An igraph object, which do not contain loop and multiple edges.

Examples

#install.packages("robin")

#If there are problems with the installation try:
# if (!requireNamespace("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install("gprege")
# install.packages("robin")   
                     
my_file <- system.file("example/football.gml", package="robin")
graph <- prepGraph(file=my_file, file.format="gml")

random

Description

This function randomly rewires the edges while preserving the original graph's degree distribution.

Usage

random(graph, dist = "NegBinom", verbose = FALSE)

Arguments

graph

The output of prepGraph.

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 "NegBinom" for negative binomial.

verbose

flag for verbose output (default as FALSE)

Value

An igraph object, a randomly rewired graph.

Examples

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

randomNoW

Description

This function randomly rewires the edges while preserving the original graph's degree distribution.

Usage

randomNoW(graph, verbose = FALSE)

Arguments

graph

The output of prepGraph.

verbose

flag for verbose output (default as FALSE)

Value

An igraph object, a randomly rewired graph.

randomWeight

Description

This function randomly rewires the edges while preserving the original graph's degree distribution.

Usage

randomWeight(graph, dist = "Other", verbose = FALSE)

Arguments

graph

The output of prepGraph.

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.

verbose

flag for verbose output (default as FALSE)

Value

An igraph object, a randomly rewired graph.

rewireCompl

Description

rewires the graph, creates the communities and compares the communities through different measures.

Usage

rewireCompl(
  data,
  number,
  community,
  method = c("walktrap", "edgeBetweenness", "fastGreedy", "louvain", "spinglass",
    "leadingEigen", "labelProp", "infomap", "optimal", "leiden", "other"),
  ...,
  measure = c("vi", "nmi", "split.join", "adjusted.rand"),
  FUN = NULL
)

Arguments

data

The output of prepGraph

number

Number of rewiring trials to perform.

community

Community to compare with.

method

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

...

additional parameters to use with any of the previous described methods (see igraph package community detection methods for more details i.e. cluster_walktrap)

measure

The measure for the comparison of the communities "vi", "nmi", "split.join", "adjusted.rand"

FUN

see methodCommunity.

rewireOnl

Description

makes the rewire function of igraph

Usage

rewireOnl(data, number)

Arguments

data

The output of prepGraph

number

Number of rewiring trials to perform.

robinAUC

Description

This function calculates the area under two curves with a spline approach.

Usage

robinAUC(x, verbose = FALSE)

Arguments

x

A robin class object. The output of the functions: robinRobust and robinCompare.

verbose

flag for verbose output (default as FALSE).

Value

A list

Examples

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

robinCompare

Description

This function compares the robustness of two community detection algorithms.

Usage

robinCompare(
  graph,
  method1 = c("walktrap", "edgeBetweenness", "fastGreedy", "leadingEigen", "louvain",
    "spinglass", "labelProp", "infomap", "optimal", "leiden", "other"),
  args1 = list(),
  method2 = c("walktrap", "edgeBetweenness", "fastGreedy", "leadingEigen", "louvain",
    "spinglass", "labelProp", "infomap", "optimal", "leiden", "other"),
  args2 = list(),
  FUN1 = NULL,
  FUN2 = NULL,
  measure = c("vi", "nmi", "split.join", "adjusted.rand"),
  type = "independent",
  verbose = TRUE,
  dist = "Other",
  BPPARAM = BiocParallel::bpparam()
)

Arguments

graph

The output of prepGraph.

method1

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

args1

A list of arguments to be passed to the method1 (see i.e. cluster_leiden for a list of possible method parameters).

method2

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

args2

A list of arguments to be passed to the method2 (see i.e. cluster_leiden for a list of possible method parameters).

FUN1

personal designed function when method1 is "other". see methodCommunity.

FUN2

personal designed function when method2 is "other". see methodCommunity.

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.

Value

A list object with two matrices: - the matrix "Mean1" with the means of the procedure for the first method - the matrix "Mean2" with the means of the procedure for the second method

Examples

my_file <- system.file("example/football.gml", package="robin")
graph <- prepGraph(file=my_file, file.format="gml")
robinCompare(graph=graph, method1="louvain", args1 = list(resolution=0.8),
            method2="leiden")

robinCompareFast

Description

This function compares two community detection algorithms. Is the parallelized and faster version of robinCompare

Usage

robinCompareFast(
  graph,
  method1 = c("walktrap", "edgeBetweenness", "fastGreedy", "leadingEigen", "louvain",
    "spinglass", "labelProp", "infomap", "optimal", "leiden", "other"),
  args1 = list(),
  method2 = c("walktrap", "edgeBetweenness", "fastGreedy", "leadingEigen", "louvain",
    "spinglass", "labelProp", "infomap", "optimal", "leiden", "other"),
  args2 = list(),
  measure = c("vi", "nmi", "split.join", "adjusted.rand"),
  FUN1 = NULL,
  FUN2 = NULL,
  verbose = TRUE,
  BPPARAM = BiocParallel::bpparam()
)

Arguments

graph

The output of prepGraph.

method1

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

args1

A list of arguments to be passed to the method1 (see i.e. cluster_leiden for a list of possible method parameters).

method2

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

args2

A list of arguments to be passed to the method2 (see i.e. cluster_leiden for a list of possible method parameters).

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.

FUN1

personal designed function when method1 is "others". see methodCommunity.

FUN2

personal designed function when method2 is "others". see methodCommunity.

verbose

flag for verbose output (default as TRUE).

BPPARAM

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

Value

A list object with two matrices: - the matrix "Mean1" with the means of the procedure for the first method - the matrix "Mean2" with the means of the procedure for the second method

robinCompareFastWeight

Description

This function compares two community detection algorithms, from weighted networks. Is the parallelized and faster version.

Usage

robinCompareFastWeight(
  graph,
  method1 = c("walktrap", "edgeBetweenness", "fastGreedy", "leadingEigen", "louvain",
    "spinglass", "labelProp", "infomap", "optimal", "leiden", "other"),
  args1 = list(),
  method2 = c("walktrap", "edgeBetweenness", "fastGreedy", "leadingEigen", "louvain",
    "spinglass", "labelProp", "infomap", "optimal", "leiden", "other"),
  args2 = list(),
  FUN1 = NULL,
  FUN2 = NULL,
  measure = c("vi", "nmi", "split.join", "adjusted.rand"),
  verbose = TRUE,
  dist = "Other",
  BPPARAM = BiocParallel::bpparam()
)

Arguments

graph

The output of prepGraph.

method1

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

args1

A list of arguments to be passed to the method1 (see i.e. cluster_leiden for a list of possible method parameters).

method2

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

args2

A list of arguments to be passed to the method2 (see i.e. cluster_leiden for a list of possible method parameters).

FUN1

personal designed function when method1 is "others". see methodCommunity.

FUN2

personal designed function when method2 is "others". see methodCommunity.

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.

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.

Value

A list object with two matrices: - the matrix "Mean1" with the means of the procedure for the first method - the matrix "Mean2" with the means of the procedure for the second method

robinCompareNoParallel

Description

This function compares the robustness of two community detection algorithms.

Usage

robinCompareNoParallel(
  graph,
  method1 = c("walktrap", "edgeBetweenness", "fastGreedy", "leadingEigen", "louvain",
    "spinglass", "labelProp", "infomap", "optimal", "leiden", "other"),
  args1 = list(),
  method2 = c("walktrap", "edgeBetweenness", "fastGreedy", "leadingEigen", "louvain",
    "spinglass", "labelProp", "infomap", "optimal", "leiden", "other"),
  args2 = list(),
  FUN1 = NULL,
  FUN2 = NULL,
  measure = c("vi", "nmi", "split.join", "adjusted.rand"),
  type = c("independent", "dependent"),
  verbose = TRUE
)

Arguments

graph

The output of prepGraph.

method1

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

args1

A list of arguments to be passed to the method1 (see i.e. cluster_leiden for a list of possible method parameters).

method2

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

args2

A list of arguments to be passed to the method2 (see i.e. cluster_leiden for a list of possible method parameters).

FUN1

personal designed function when method1 is "other". see methodCommunity.

FUN2

personal designed function when method2 is "other". see methodCommunity.

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).

Value

A list object with two matrices: - the matrix "Mean1" with the means of the procedure for the first method - the matrix "Mean2" with the means of the procedure for the second method

robinFDATest

Description

The function implements the Interval Testing Procedure to test the difference between two curves.

Usage

robinFDATest(x, verbose = FALSE)

Arguments

x

A robin class object. The output of the functions: robinRobust and robinCompare.

verbose

flag for verbose output (default as FALSE).

Value

Two plots: the fitted curves and the adjusted p-values. A vector of the adjusted p-values.

Examples

my_file <- system.file("example/football.gml", package="robin")
graph <- prepGraph(file=my_file, file.format="gml")
comp <- robinCompare(graph=graph, method1="fastGreedy",method2="infomap")
robinFDATest(comp)

robinGPTest

Description

This function implements the GP testing procedure and calculates the Bayes factor.

Usage

robinGPTest(x, verbose = FALSE)

Arguments

x

A robin class object. The output of the functions: robinRobust and robinCompare.

verbose

flag for verbose output (default as FALSE).

Value

A numeric value, the Bayes factor

Examples

my_file <- system.file("example/football.gml", package="robin")
graph <- prepGraph(file=my_file, file.format="gml")
comp <- robinCompare(graph=graph, method1="fastGreedy",method2="infomap")
robinGPTest(comp)

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()
)

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.

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.

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")

robinRobustFast

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

robinRobustFast(
  graph,
  graphRandom,
  method = c("walktrap", "edgeBetweenness", "fastGreedy", "louvain", "spinglass",
    "leadingEigen", "labelProp", "infomap", "optimal", "leiden", "other"),
  ...,
  FUN1 = NULL,
  measure = c("vi", "nmi", "split.join", "adjusted.rand"),
  verbose = TRUE,
  BPPARAM = BiocParallel::bpparam()
)

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

FUN1

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.

verbose

flag for verbose output (default as TRUE)

BPPARAM

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

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.

robinRobustFastWeighted

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 for weighted network.

Usage

robinRobustFastWeighted(
  graph,
  graphRandom,
  method = c("walktrap", "edgeBetweenness", "fastGreedy", "louvain", "spinglass",
    "leadingEigen", "labelProp", "infomap", "optimal", "leiden", "other"),
  ...,
  FUN1 = NULL,
  measure = c("vi", "nmi", "split.join", "adjusted.rand"),
  verbose = TRUE,
  dist = "Other",
  BPPARAM = BiocParallel::bpparam()
)

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

FUN1

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.

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.

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.

robinRobustNoParallel

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

robinRobustNoParallel(
  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 = c("independent", "dependent"),
  verbose = TRUE
)

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 procedure.

verbose

flag for verbose output (default as TRUE).

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.