sample_lfr {netUtils} | R Documentation |
LFR benchmark graphs
Description
Generates benchmark networks for clustering tasks with a priori known communities. The algorithm accounts for the heterogeneity in the distributions of node degrees and of community sizes.
Usage
sample_lfr(
n,
tau1,
tau2,
mu,
average_degree = NULL,
max_degree = NULL,
min_community = NULL,
max_community = NULL,
on = 0,
om = 0
)
Arguments
n |
Number of nodes in the created graph. |
tau1 |
Power law exponent for the degree distribution of the created graph. This value must be strictly greater than one |
tau2 |
Power law exponent for the community size distribution in the created graph. This value must be strictly greater than one |
mu |
Fraction of inter-community edges incident to each node. This value must be in the interval 0 to 1. |
average_degree |
Desired average degree of nodes in the created graph. This value must be in the interval 0 to n. Exactly one of this and |
max_degree |
Maximum degree of nodes in the created graph. If not specified, this is set to n-1. |
min_community |
Minimum size of communities in the graph. If not specified, this is set to |
max_community |
Maximum size of communities in the graph. If not specified, this is set to n, the total number of nodes in the graph. |
on |
number of overlapping nodes |
om |
number of memberships of the overlapping nodes |
Details
code adapted from https://github.com/synwalk/synwalk-analysis/tree/master/lfr_generator
Value
an igraph object
References
A. Lancichinetti, S. Fortunato, and F. Radicchi.(2008) Benchmark graphs for testing community detection algorithms. Physical Review E, 78. arXiv:0805.4770
Examples
# Simple Girven-Newman benchmark graphs
g <- sample_lfr(n = 128,average_degree = 16,
max_degree = 16,mu = 0.1,
min_community = 32,max_community = 32)