sample_last_cit: Random citation graphs

Description

sample_last_cit creates a graph, where vertices age, and gain new connections based on how long ago their last citation happened.

Usage

sample_last_cit(
  n,
  edges = 1,
  agebins = n/7100,
  pref = (1:(agebins + 1))^-3,
  directed = TRUE
)
last_cit(...)
sample_cit_types(
  n,
  edges = 1,
  types = rep(0, n),
  pref = rep(1, length(types)),
  directed = TRUE,
  attr = TRUE
)
cit_types(...)
sample_cit_cit_types(
  n,
  edges = 1,
  types = rep(0, n),
  pref = matrix(1, nrow = length(types), ncol = length(types)),
  directed = TRUE,
  attr = TRUE
)
cit_cit_types(...)

Value

A new graph.

Arguments

n: Number of vertices.
edges: Number of edges per step.
agebins: Number of aging bins.
pref: Vector (sample_last_cit and sample_cit_types or matrix (sample_cit_cit_types) giving the (unnormalized) citation probabilities for the different vertex types.
directed: Logical scalar, whether to generate directed networks.
...: Passed to the actual constructor.
types: Vector of length ‘n’, the types of the vertices. Types are numbered from zero.
attr: Logical scalar, whether to add the vertex types to the generated graph as a vertex attribute called ‘type’.

Author

Gabor Csardi csardi.gabor@gmail.com

Details

sample_cit_cit_types is a stochastic block model where the graph is growing.

sample_cit_types is similarly a growing stochastic block model, but the probability of an edge depends on the (potentially) cited vertex only.