Random citation graphsSource:
sample_last_cit() creates a graph, where vertices age, and
gain new connections based on how long ago their last citation
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(...)
Number of vertices.
Number of edges per step.
Number of aging bins.
sample_cit_types()or matrix (
sample_cit_cit_types()) giving the (unnormalized) citation probabilities for the different vertex types.
Logical scalar, whether to generate directed networks.
Passed to the actual constructor.
Vector of length ‘
n’, the types of the vertices. Types are numbered from zero.
Logical scalar, whether to add the vertex types to the generated graph as a vertex attribute called ‘
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
Random graph models (games)
Gabor Csardi firstname.lastname@example.org