static.power.law.game: Scale-free random graphs, from vertex fitness scores

In igraph/rigraph: Network Analysis and Visualization

Scale-free random graphs, from vertex fitness scores

Description

static.power.law.game() was renamed to sample_fitness_pl() to create a more consistent API.

Usage

static.power.law.game(
  no.of.nodes,
  no.of.edges,
  exponent.out,
  exponent.in = -1,
  loops = FALSE,
  multiple = FALSE,
  finite.size.correction = TRUE
)

Arguments

no.of.nodes	The number of vertices in the generated graph.
no.of.edges	The number of edges in the generated graph.
exponent.out	Numeric scalar, the power law exponent of the degree distribution. For directed graphs, this specifies the exponent of the out-degree distribution. It must be greater than or equal to 2. If you pass Inf here, you will get back an Erdős-Rényi random network.
exponent.in	Numeric scalar. If negative, the generated graph will be undirected. If greater than or equal to 2, this argument specifies the exponent of the in-degree distribution. If non-negative but less than 2, an error will be generated.
loops	Logical scalar, whether to allow loop edges in the generated graph.
multiple	Logical scalar, whether to allow multiple edges in the generated graph.
finite.size.correction	Logical scalar, whether to use the proposed finite size correction of Cho et al., see references below.

igraph/rigraph documentation built on May 19, 2024, 6:19 a.m.