Package 'leidenAlg'

Returns pre-calculated dendrogram

Description

Returns pre-calculated dendrogram

Usage

## S3 method for class 'fakeCommunities'
as.dendrogram(object, ...)

Arguments

object	fakeCommunities object
...	further parameters for generic

Value

dendrogram

Examples

rLeidenComm = suppressWarnings(rleiden.community(exampleGraph, n.cores=1))
as.dendrogram.fakeCommunities(rLeidenComm)

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Conos graph

Description

Conos graph

Usage

exampleGraph

Format

An object of class igraph of length 100.

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Finds the optimal partition using the Leiden algorithm

Description

Finds the optimal partition using the Leiden algorithm

Usage

find_partition(graph, edge_weights, resolution = 1, niter = 2)

Arguments

graph	The igraph graph to define the partition on
edge_weights	Vector of edge weights. In weighted graphs, a real number is assigned to each (directed or undirected) edge. For an unweighted graph, this is set to 1. Refer to igraph, weighted graphs.
resolution	Numeric scalar, resolution parameter controlling communities detected (default=1.0) Higher resolutions lead to more communities, while lower resolutions lead to fewer communities.
niter	Number of iterations that the algorithm should be run for (default=2)

Value

A vector of membership values

Examples

library(igraph)
library(leidenAlg)

g <- make_star(10)
E(g)$weight <- seq(ecount(g))
find_partition(g, E(g)$weight)

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Refer to the R function find_partition() For notes of the graph object, refer to https://igraph.org/c/doc/igraph-Basic.html

Description

Refer to the R function find_partition() For notes of the graph object, refer to https://igraph.org/c/doc/igraph-Basic.html

Usage

find_partition_rcpp(
  edgelist,
  edgelist_length,
  num_vertices,
  direction,
  edge_weights,
  resolution = 1,
  niter = 2L
)

Arguments

edgelist	The graph edge list
edgelist_length	integer The length of the graph edge list
num_vertices	integer The number of vertices in the graph
direction	boolean Whether the graph is directed or undirected
edge_weights	Vector of edge weights. In weighted graphs, a real number is assigned to each (directed or undirected) edge. For an unweighted graph, this is set to 1. Refer to igraph, weighted graphs.
resolution	Numeric scalar, resoluiton parameter controlling communities detected (default=1.0) Higher resolutions lead to more communities, while lower resolutions lead to fewer communities.
niter	Number of iterations that the algorithm should be run for (default=2)

Value

A vector of membership values

Examples

library(igraph)
edgelist <- as.vector(t(igraph::as_edgelist(exampleGraph, names=FALSE))) - 1
edgelist_length <- length(edgelist)
num_vertices <- length(igraph::V(exampleGraph)) - 1
direction <- igraph::is_weighted(exampleGraph)
find_partition_rcpp(edgelist, edgelist_length, num_vertices, direction, E(exampleGraph)$weight)

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Finds the optimal partition using the Leiden algorithm with replicate starts

Description

This function performs Leiden algorithm nrep times and returns the result from the run with the maximum quality.

Since Leiden algorithm has stochastic process, repeating stochastically may improve the result. However, users should be aware of whether there is indeed a community structure with exploration, rather than blindly trusting the returned result that comes with the highest quality value.

The random number generator (RNG) is not re-seeded at each new start of community detection, in order to keep the independence of each replicate. To get reproducible result, users can run set.seed() before calling these functions.

find_partition only performs the community detection once and the reproducibility can also be ensured with set.seed().

Usage

find_partition_with_rep(
  graph,
  edge_weights,
  resolution = 1,
  niter = 2,
  nrep = 10
)

Arguments

graph	The igraph graph to define the partition on
edge_weights	Vector of edge weights. In weighted graphs, a real number is assigned to each (directed or undirected) edge. For an unweighted graph, this is set to 1. Refer to igraph, weighted graphs.
resolution	Numeric scalar, resolution parameter controlling communities detected (default=1.0) Higher resolutions lead to more communities, while lower resolutions lead to fewer communities.
niter	Number of iterations that the algorithm should be run for (default=2)
nrep	Number of replicate starts with random number being updated. (default=10) The result with the best quality will be returned.

Value

A vector of membership values

Examples

library(igraph)

# To run 10 replicates and get the partitioning with the highest quality
membership <- find_partition_with_rep(exampleGraph, E(exampleGraph)$weight, nrep = 10)

# To get reprodicible result for every function call, do `set.seed()` right before calling
set.seed(233)
res1 <- find_partition_with_rep(exampleGraph, E(exampleGraph)$weight, resolution = 2)
# Here, no seed was set...
res2 <- find_partition_with_rep(exampleGraph, E(exampleGraph)$weight, resolution = 2)
set.seed(233)
res3 <- find_partition_with_rep(exampleGraph, E(exampleGraph)$weight, resolution = 2)
identical(res1, res2) # FALSE (usually), as no seed as set
identical(res1, res3) # TRUE (always), as set.seed() was used directly before the function call

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Finds the optimal partition using the Leiden algorithm

Description

Finds the optimal partition using the Leiden algorithm

Usage

find_partition_with_rep_rcpp(
  edgelist,
  edgelist_length,
  num_vertices,
  direction,
  edge_weights,
  resolution = 1,
  niter = 2L,
  nrep = 1L
)

Arguments

edgelist	The graph edge list
edgelist_length	integer The length of the graph edge list
num_vertices	integer The number of vertices in the graph
direction	boolean Whether the graph is directed or undirected
edge_weights	Vector of edge weights. In weighted graphs, a real number is assigned to each (directed or undirected) edge. For an unweighted graph, this is set to 1. Refer to igraph, weighted graphs.
resolution	Numeric scalar, resoluiton parameter controlling communities detected (default=1.0) Higher resolutions lead to more communities, while lower resolutions lead to fewer communities.
niter	Number of iterations that the algorithm should be run for (default=2)
nrep	Number of replicate starts with random number being updated. (default=10) The result with the best quality will be returned.

Details

For notes of the graph object, refer to https://igraph.org/c/doc/igraph-Basic.html

Examples

library(igraph)

edgelist <- as.vector(t(igraph::as_edgelist(exampleGraph, names=FALSE))) - 1
edgelist_len <- length(edgelist)  ## The length of the graph edge list
n_vertices <- length(igraph::V(exampleGraph)) - 1  ## The number of vertices in the graph
direct <- igraph::is_weighted(exampleGraph)  ## Whether the graph is directed or undirected
edge_weights <- E(exampleGraph)$weight
find_partition_with_rep_rcpp(edgelist, edgelist_len, n_vertices, direct, edge_weights, nrep = 10)

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Leiden algorithm community detection Detects communities using Leiden algorithm (implementation copied from https://github.com/vtraag/leidenalg)

Description

Leiden algorithm community detection Detects communities using Leiden algorithm (implementation copied from https://github.com/vtraag/leidenalg)

Usage

leiden.community(graph, resolution = 1, n.iterations = 2)

Arguments

graph	graph on which communities should be detected
resolution	resolution parameter (default=1.0) - higher numbers lead to more communities
n.iterations	number of iterations that the algorithm should be run for (default=2)

Value

a fakeCommunities object that returns membership and dendrogram

Examples

leiden.community(exampleGraph)

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Returns pre-calculated membership factor

Description

Returns pre-calculated membership factor

Usage

## S3 method for class 'fakeCommunities'
membership(communities, ...)

Arguments

communities	fakeCommunities object
...	further parameters for generic

Value

membership factor

Examples

leidenComm = leiden.community(exampleGraph)
membership.fakeCommunities(leidenComm)

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Recursive leiden communities Constructs an n-step recursive clustering, using leiden.community

Description

Recursive leiden communities Constructs an n-step recursive clustering, using leiden.community

Usage

rleiden.community(
  graph,
  max.depth = 2,
  n.cores = parallel::detectCores(logical = FALSE),
  min.community.size = 10,
  verbose = FALSE,
  resolution = 1,
  cur.depth = 1,
  hierarchical = TRUE,
  ...
)

Arguments

graph	graph
max.depth	Recursive depth (default=2)
n.cores	integer Number of cores to use (default = parallel::detectCores(logical=FALSE)). If logical=FALSE, uses the number of physical CPUs/cores. If logical=TRUE, uses the logical number of CPUS/cores. See parallel::detectCores()
min.community.size	integer Minimal community size parameter for the walktrap communities—Communities smaller than that will be merged (default=10)
verbose	boolean Whether to output progress messages (default=FALSE)
resolution	resolution parameter passed to leiden.community (either a single value, or a value equivalent to max.depth) (default=1)
cur.depth	integer Current depth of clustering (default=1)
hierarchical	boolean If TRUE, calculate hierarchy on the multilevel clusters (default=TRUE)
...	passed to leiden.community

Value

a fakeCommunities object that returns membership and dendrogram

Examples

rleiden.community(exampleGraph, n.cores=1)

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