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(...)Arguments
- n
Number of vertices.
- edges
Number of edges per step.
- agebins
Number of aging bins.
- pref
Vector (
sample_last_cit()andsample_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’.
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.
See also
Random graph models (games)
bipartite_gnm(),
erdos.renyi.game(),
sample_(),
sample_bipartite(),
sample_chung_lu(),
sample_correlated_gnp(),
sample_correlated_gnp_pair(),
sample_degseq(),
sample_dot_product(),
sample_fitness(),
sample_fitness_pl(),
sample_forestfire(),
sample_gnm(),
sample_gnp(),
sample_grg(),
sample_growing(),
sample_hierarchical_sbm(),
sample_islands(),
sample_k_regular(),
sample_pa(),
sample_pa_age(),
sample_pref(),
sample_sbm(),
sample_smallworld(),
sample_traits_callaway(),
sample_tree()
Author
Gabor Csardi csardi.gabor@gmail.com