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)
erdos.renyi.game()
,
sample_()
,
sample_bipartite()
,
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