| leiden_find_partition {leidenbase} | R Documentation |
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)