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############### COMPARE METHOD ##########
#' robinCompare
#'
#' @description This function compares the robustness of two community
#' detection algorithms.
#' @param graph The output of prepGraph.
#' @param method1 The first clustering method, one of "walktrap",
#' "edgeBetweenness", "fastGreedy", "louvain", "spinglass", "leadingEigen",
#' "labelProp", "infomap","leiden","optimal","other".
#' @param args1 A \code{list} of arguments to be passed to the \code{method1}
#' (see i.e. \link[igraph]{cluster_leiden} for a list of possible method parameters).
#' @param method2 The second custering method one of "walktrap",
#' "edgeBetweenness","fastGreedy", "louvain", "spinglass", "leadingEigen",
#' "labelProp", "infomap","leiden","optimal","other".
#' @param args2 A \code{list} of arguments to be passed to the \code{method2}
#' (see i.e. \link[igraph]{cluster_leiden} for a list of possible method parameters).
#' @param FUN1 personal designed function when \code{method1} is "other".
#' see \code{\link{methodCommunity}}.
#' @param FUN2 personal designed function when \code{method2} is "other".
#' see \code{\link{methodCommunity}}.
#' @param 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.
#' @param type The type of robin construction, dependent or independent.
#' @param 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
#' \code{\link[perturbR]{rewireR}} for details.
#' @param BPPARAM the BiocParallel object of class \code{bpparamClass} that
#' specifies the back-end to be used for computations. See
#' \code{\link[BiocParallel]{bpparam}} for details.
#' @param verbose flag for verbose output (default as TRUE).
#'
#'
#' @return 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
#'
#' @import igraph
#' @export
#'
#' @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")
## Weighted example:
# E(graph)$weight <- round(runif(ecount(graph),min=1,max=10))
# robinCompare(graph=graph, method1="louvain", args1 = list(resolution=0.8),
# method2="leiden", dist="NegBinom")
robinCompare <- function(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())
{
methods <- c(method1, method2)
# Weigthed version
if ( is_weighted(graph) )
{
print("Weighted Network Parallel Function")
output <- robinCompareFastWeight(graph=graph, method1=method1, args1=args1,
method2=method2, args2=args2, FUN1=FUN1, FUN2=FUN2, measure=measure,
verbose=verbose, dist=dist,BPPARAM=BPPARAM)
} else {
if(type=="dependent")
{
print("Unweighted Network No Parallel Function")
output <- robinCompareNoParallel(graph=graph, method1=method1, args1=args1,
method2=method2, args2=args2, measure=measure,
type=type)
}else{
print("Unweighted Network Parallel Function")
output <- robinCompareFast(graph=graph, method1=method1, args1=args1,
method2=method2, args2=args2,
FUN1=FUN1, FUN2=FUN2, measure=measure,
verbose=verbose,BPPARAM=BPPARAM)
}
}
outputRobin <- c(output, model=methods, list(graph=graph))
class(outputRobin) <- "robin"
return(outputRobin)
}
############### ROBUST METHOD ##########
#' 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.
#' @param graph The output of prepGraph.
#' @param graphRandom The output of random function.
#' @param method The clustering method, one of "walktrap", "edgeBetweenness",
#' "fastGreedy", "louvain", "spinglass", "leadingEigen", "labelProp", "infomap",
#' "leiden","optimal".
#' @param 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.
#' @param 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.
#' @param type The type of robin construction, dependent or independent.
#' @param ... other parameter.
#' @param 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
#' \code{\link[perturbR]{rewireR}} for details.
#' @param BPPARAM the BiocParallel object of class \code{bpparamClass} that
#' specifies the back-end to be used for computations. See
#' \code{\link[BiocParallel]{bpparam}} for details.
#' @param verbose flag for verbose output (default as TRUE).
#'
#' @return 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.
#'
#' @import igraph
#' @export
#'
#' @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")
## Weighted Example:
# E(graph)$weight <- round(runif(ecount(graph),min=1,max=10))
# graphRandom <- random(graph=graph)
# robinRobust(graph=graph, graphRandom=graphRandom, method="leiden",
# dist="NegBinom")
robinRobust <- function(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())
{
methods <- c("real data", "null model")
# Weigthed version
if ( is_weighted(graph) )
{
print("Weighted Network Parallel Function")
output <- robinRobustFastWeighted(graph=graph, graphRandom=graphRandom,
method=method,
...,
FUN1=FUN, measure=measure,
verbose=verbose,
dist=dist,BPPARAM=BPPARAM)
} else {
if(type=="dependent")
{
print("Unweighted Network No Parallel Function")
# No Parallel
output <- robinRobustNoParallel(graph=graph, graphRandom= graphRandom,
method=method,
...,
FUN=FUN, measure=measure,
type=type, verbose=verbose)
}else{
print("Unweighted Network Parallel Function")
# Parallel version:
output <- robinRobustFast(graph=graph, graphRandom=graphRandom,
method=method,
...,
FUN1=FUN, measure=measure,
verbose=verbose,BPPARAM=BPPARAM)
}
}
outputRobin <- c(output, model=methods, list(graph=graph))
class(outputRobin) <- "robin"
return(outputRobin)
}
####### GRAPH RANDOM #########
#' random
#'
#' @description This function randomly rewires the edges while preserving the original graph's
#' degree distribution.
#' @param graph The output of prepGraph.
#' @param 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.
#' @param verbose flag for verbose output (default as FALSE)
#'
#' @return An igraph object, a randomly rewired graph.
#' @import igraph
#' @export
#'
#' @examples
#' my_file <- system.file("example/football.gml", package="robin")
#' graph <- prepGraph(file=my_file, file.format="gml")
#' graphRandom <- random(graph=graph)
random <- function(graph, dist="NegBinom", verbose=FALSE)
{
# Weigthed version
if ( is.weighted(graph) )
{
graphRandom <- randomWeight(graph=graph, dist=dist,
verbose=verbose)
}else{
graphRandom <- randomNoW(graph=graph, verbose=verbose)
}
return(graphRandom)
}
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