sample_last_cit {igraph} R Documentation

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(...)


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’.

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.

A new graph.

Author(s)

Gabor Csardi csardi.gabor@gmail.com

Random graph models (games) erdos.renyi.game(), sample_bipartite(), sample_correlated_gnp_pair(), sample_correlated_gnp(), sample_degseq(), sample_dot_product(), sample_fitness_pl(), sample_fitness(), sample_forestfire(), sample_gnm(), sample_gnp(), sample_grg(), sample_growing(), sample_hierarchical_sbm(), sample_islands(), sample_k_regular(), sample_pa_age(), sample_pa(), sample_pref(), sample_sbm(), sample_smallworld(), sample_traits_callaway(), sample_tree(), sample_()

Random graph models (games) erdos.renyi.game(), sample_bipartite(), sample_correlated_gnp_pair(), sample_correlated_gnp(), sample_degseq(), sample_dot_product(), sample_fitness_pl(), sample_fitness(), sample_forestfire(), sample_gnm(), sample_gnp(), sample_grg(), sample_growing(), sample_hierarchical_sbm(), sample_islands(), sample_k_regular(), sample_pa_age(), sample_pa(), sample_pref(), sample_sbm(), sample_smallworld(), sample_traits_callaway(), sample_tree(), sample_()

Random graph models (games) erdos.renyi.game(), sample_bipartite(), sample_correlated_gnp_pair(), sample_correlated_gnp(), sample_degseq(), sample_dot_product(), sample_fitness_pl(), sample_fitness(), sample_forestfire(), sample_gnm(), sample_gnp(), sample_grg(), sample_growing(), sample_hierarchical_sbm(), sample_islands(), sample_k_regular(), sample_pa_age(), sample_pa(), sample_pref(), sample_sbm(), sample_smallworld(), sample_traits_callaway(), sample_tree(), sample_()

Random graph models (games) erdos.renyi.game(), sample_bipartite(), sample_correlated_gnp_pair(), sample_correlated_gnp(), sample_degseq(), sample_dot_product(), sample_fitness_pl(), sample_fitness(), sample_forestfire(), sample_gnm(), sample_gnp(), sample_grg(), sample_growing(), sample_hierarchical_sbm(), sample_islands(), sample_k_regular(), sample_pa_age(), sample_pa(), sample_pref(), sample_sbm(), sample_smallworld(), sample_traits_callaway(), sample_tree(), sample_()

Random graph models (games) erdos.renyi.game(), sample_bipartite(), sample_correlated_gnp_pair(), sample_correlated_gnp(), sample_degseq(), sample_dot_product(), sample_fitness_pl(), sample_fitness(), sample_forestfire(), sample_gnm(), sample_gnp(), sample_grg(), sample_growing(), sample_hierarchical_sbm(), sample_islands(), sample_k_regular(), sample_pa_age(), sample_pa(), sample_pref(), sample_sbm(), sample_smallworld(), sample_traits_callaway(), sample_tree(), sample_()

Random graph models (games) erdos.renyi.game(), sample_bipartite(), sample_correlated_gnp_pair(), sample_correlated_gnp(), sample_degseq(), sample_dot_product(), sample_fitness_pl(), sample_fitness(), sample_forestfire(), sample_gnm(), sample_gnp(), sample_grg(), sample_growing(), sample_hierarchical_sbm(), sample_islands(), sample_k_regular(), sample_pa_age(), sample_pa(), sample_pref(), sample_sbm(), sample_smallworld(), sample_traits_callaway(), sample_tree(), sample_()

[Package igraph version 1.5.1 Index]