leiden: Run Leiden clustering algorithm

In leiden: R Implementation of Leiden Clustering Algorithm

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 igraph object or an input graph as an igraph object (e.g., shared nearest neighbours). A list of multiple graph objects can be passed for multiplex community detection.
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)
}

leiden documentation built on Nov. 17, 2023, 5:08 p.m.