Package 'leidenbase'

Leiden find partition community detection function

Description

R to C wrapper that runs the basic Leiden community detection algorithm, which is similar to the find_partition() function in the python Leidenalg distribution.

Usage

leiden_find_partition(
  igraph,
  partition_type = c("CPMVertexPartition", "ModularityVertexPartition",
    "RBConfigurationVertexPartition", "RBERVertexPartition",
    "SignificanceVertexPartition", "SurpriseVertexPartition"),
  initial_membership = NULL,
  edge_weights = NULL,
  node_sizes = NULL,
  seed = NULL,
  resolution_parameter = 0.1,
  num_iter = 2,
  verbose = FALSE
)

Arguments

igraph	R igraph graph.
partition_type	String partition type name. Default is CPMVertexParition.
initial_membership	Numeric vector of initial membership assignments of nodes. These are 1-based indices. Default is one community per node.
edge_weights	Numeric vector of edge weights. Default is 1.0 for all edges.
node_sizes	Numeric vector of node sizes. Default is 1 for all nodes.
seed	Numeric random number generator seed. The seed value must be either NULL for random seed values or greater than 0 for a fixed seed value. Default is NULL.
resolution_parameter	Numeric resolution parameter. The value must be greater than 0.0. Default is 0.1. The resolution_parameter is ignored for the partition_types ModularityVertexPartition, SignificanceVertexPartition, and SurpriseVertexPartition.
num_iter	Numeric number of iterations. Default is 2.
verbose	A logic flag to determine whether or not we should print run diagnostics.

Details

The Leiden algorithm is described in From Louvain to Leiden: guaranteeing well-connected communities. V. A. Traag and L. Waltman and N. J. van Eck Scientific Reports, 9(1) (2019) DOI: 10.1038/s41598-019-41695-z.

Significance is described in Significant Scales in Community Structure V. A. Traag, G. Krings, and P. Van Dooren Scientific Reports, 3(1) (2013) DOI: 10.1038/srep02930

Notes excerpted from leidenalg/src/VertexPartition.py

• CPMVertexPartition Implements Constant Potts Model. This quality function uses a linear resolution parameter and is well-defined for both positive and negative edge weights.

• ModularityVertexPartition Implements modularity. This quality function is well-defined only for positive edge weights.

• RBConfigurationVertexPartition Implements Reichardt and Bornholdt’s Potts model with a configuration null model. This quality function uses a linear resolution parameter and is well-defined only for positive edge weights.

• RBERVertexPartition Implements Reichardt and Bornholdt’s Potts model with an Erdos-Renyi null model. This quality function uses a linear resolution parameter and is well-defined only for positive edge weights.

• SignificanceVertexPartition Implements Significance. This quality function is well-defined only for unweighted graphs.

• SurpriseVertexPartition Implements (asymptotic) Surprise. This quality function is well-defined only for positive edge weights.

Value

A named list consisting of a numeric vector of the node community memberships (1-based indices), a numeric quality value, a numeric modularity, a numeric significance, a numeric vector of edge weights within each community, a numeric vector of edge weights from each community, a numeric vector of edge weights to each community, and total edge weight in the graph.

References

V. A. Traag, L. Waltman, N. J. van Eck (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 9(1). DOI: 10.1038/s41598-019-41695-z

Significant Scales in Community Structure V. A. Traag, G. Krings, and P. Van Dooren Scientific Reports, 3(1) (2013) DOI: 10.1038/srep02930

Examples

library(igraph)
  fpath <- system.file( 'testdata', 'igraph_n1500_edgelist.txt.gz', package = 'leidenbase' )
  zfp <- gzfile(fpath)
  igraph <- read_graph( file = zfp, format='edgelist', n=1500 )
  res <- leiden_find_partition(igraph=igraph,
                               partition_type='CPMVertexPartition',
                               resolution_parameter=1e-5)

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