| Type: | Package |
| Title: | R Implementation of Leiden Clustering Algorithm |
| Version: | 0.4.3.1 |
| Date: | 2023-11-08 |
| Description: | Implements the 'Python leidenalg' module to be called in R. Enables clustering using the leiden algorithm for partition a graph into communities. See the 'Python' repository for more details: https://github.com/vtraag/leidenalg Traag et al (2018) From Louvain to Leiden: guaranteeing well-connected communities. <doi:10.48550/arXiv.1810.08473>. |
| License: | GPL-3 | file LICENSE |
| URL: | https://github.com/TomKellyGenetics/leiden |
| BugReports: | https://github.com/TomKellyGenetics/leiden/issues |
| Imports: | methods, reticulate, Matrix, igraph (≥ 1.2.7) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| Suggests: | bipartite, covr, data.table, devtools, graphsim, knitr, markdown, multiplex, multinet, network, qpdf, RColorBrewer, remotes, rmarkdown, spelling, testthat, tibble |
| Language: | en-US |
| VignetteBuilder: | knitr |
| Collate: | 'find_partition.R' 'leiden.R' 'py_objects.R' |
| NeedsCompilation: | no |
| Packaged: | 2023-11-13 08:25:25 UTC; kelly-t |
| Author: | S. Thomas Kelly [aut, cre, trl], Vincent A. Traag [com] |
| Maintainer: | S. Thomas Kelly <tomkellygenetics@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-11-17 10:20:23 UTC |
Run Leiden clustering algorithm
Description
Implements the Leiden clustering algorithm in R using reticulate to run the Python version. Requires the python "leidenalg" and "igraph" modules to be installed. Returns a vector of partition indices. Windows users can still this with devtools::install_github("rstudio/reticulate", ref = "86ebb56"); reticulate::use_condaenv("r-reticulate"); reticulate::conda_install("r-reticulate", "leidenalg", channel = "vtraag")
Usage
leiden(
object,
partition_type = c("RBConfigurationVertexPartition", "ModularityVertexPartition",
"RBERVertexPartition", "CPMVertexPartition", "MutableVertexPartition",
"SignificanceVertexPartition", "SurpriseVertexPartition",
"ModularityVertexPartition.Bipartite", "CPMVertexPartition.Bipartite"),
initial_membership = NULL,
weights = NULL,
node_sizes = NULL,
resolution_parameter = 1,
seed = NULL,
n_iterations = 2L,
max_comm_size = 0L,
degree_as_node_size = FALSE,
laplacian = FALSE,
legacy = FALSE
)
Arguments
object |
An adjacency matrix compatible with |
partition_type |
Type of partition to use. Defaults to RBConfigurationVertexPartition. Options include: ModularityVertexPartition, RBERVertexPartition, CPMVertexPartition, MutableVertexPartition, SignificanceVertexPartition, SurpriseVertexPartition, ModularityVertexPartition.Bipartite, CPMVertexPartition.Bipartite (see the Leiden python module documentation for more details) |
initial_membership, weights, node_sizes |
Parameters to pass to the Python leidenalg function (defaults initial_membership=None, weights=None). Weights are derived from weighted igraph objects and non-zero integer values of adjacency matrices. |
resolution_parameter |
A parameter controlling the coarseness of the clusters |
seed |
Seed for the random number generator. By default uses a random seed if nothing is specified. |
n_iterations |
Number of iterations to run the Leiden algorithm. By default, 2 iterations are run. If the number of iterations is negative, the Leiden algorithm is run until an iteration in which there was no improvement. |
max_comm_size |
(non-negative int) – Maximal total size of nodes in a community. If zero (the default), then communities can be of any size. |
degree_as_node_size |
(defaults to FALSE). If True use degree as node size instead of 1, to mimic modularity for Bipartite graphs. |
laplacian |
(defaults to FALSE). Derive edge weights from the Laplacian matrix. |
legacy |
(defaults to FALSE). Force calling python implementation via reticulate. Default behaviour is calling cluster_leiden in igraph with Modularity (for undirected graphs) and CPM cost functions. |
Value
A partition of clusters as a vector of integers
Examples
#check if python is availble
modules <- reticulate::py_module_available("leidenalg") && reticulate::py_module_available("igraph")
if(modules){
#generate example data
adjacency_matrix <- rbind(cbind(matrix(round(rbinom(4000, 1, 0.8)), 20, 20),
matrix(round(rbinom(4000, 1, 0.3)), 20, 20),
matrix(round(rbinom(400, 1, 0.1)), 20, 20)),
cbind(matrix(round(rbinom(400, 1, 0.3)), 20, 20),
matrix(round(rbinom(400, 1, 0.8)), 20, 20),
matrix(round(rbinom(4000, 1, 0.2)), 20, 20)),
cbind(matrix(round(rbinom(400, 1, 0.3)), 20, 20),
matrix(round(rbinom(4000, 1, 0.1)), 20, 20),
matrix(round(rbinom(4000, 1, 0.9)), 20, 20)))
rownames(adjacency_matrix) <- 1:60
colnames(adjacency_matrix) <- 1:60
#generate partitions
partition <- leiden(adjacency_matrix)
table(partition)
#generate partitions at a lower resolution
partition <- leiden(adjacency_matrix, resolution_parameter = 0.5)
table(partition)
#generate example weights
weights <- sample(1:10, sum(adjacency_matrix!=0), replace=TRUE)
partition <- leiden(adjacency_matrix, weights = weights)
table(partition)
#generate example weighted matrix
adjacency_matrix[adjacency_matrix == 1] <- weights
partition <- leiden(adjacency_matrix)
table(partition)
# generate (unweighted) igraph object in R
library("igraph")
adjacency_matrix[adjacency_matrix > 1] <- 1
my_graph <- graph_from_adjacency_matrix(adjacency_matrix)
partition <- leiden(my_graph)
table(partition)
# generate (weighted) igraph object in R
library("igraph")
adjacency_matrix[adjacency_matrix >= 1] <- weights
my_graph <- graph_from_adjacency_matrix(adjacency_matrix, weighted = TRUE)
partition <- leiden(my_graph)
table(partition)
# pass weights to python leidenalg
adjacency_matrix[adjacency_matrix >= 1 ] <- 1
my_graph <- graph_from_adjacency_matrix(adjacency_matrix, weighted = NULL)
weights <- sample(1:10, sum(adjacency_matrix!=0), replace=TRUE)
partition <- leiden(my_graph, weights = weights)
table(partition)
# run only if python is available (for testing)
}