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LFR benchmark graphs

Source: R/lfr_benchmark.R
sample_lfr.Rd

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 = 2,
  tau2 = 1,
  mu = 0.1,
  average_degree,
  max_degree,
  min_community = NULL,
  max_community = NULL,
  on = 0,
  om = 0,
  verbose = FALSE
)

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 at least one.

tau2

Power law exponent for the community size distribution in the created graph. This value must be at least 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, n] and is required.

max_degree

Maximum degree of nodes in the created graph. This value must be in the interval (0, n] and is required.

min_community

Minimum size of communities in the graph. Either both or none of min_community and max_community must be specified. If none are specified, the community size range is set automatically to [max(k_min, 3), max_degree], where k_min is the minimum degree implied by average_degree, max_degree and tau1.

max_community

Maximum size of communities in the graph. Must be at least min_community.

on

number of overlapping nodes (a non-negative integer not larger than n).

om

number of memberships of the overlapping nodes. Must be at least 2 if on > 0.

verbose

logical. Should progress messages of the generator be printed?

Value

an igraph object with two vertex attributes: membership, an integer vector holding the (first) community of each vertex, and memberships, a list holding all communities of each vertex (only overlapping vertices have more than one).

Details

code adapted from https://github.com/synwalk/synwalk-analysis/tree/master/lfr_generator. Random numbers are drawn from R's random number generator, so results can be reproduced with set.seed().

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
)