Random citation graphs
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)
sample_cit_types(n, edges = 1, types = rep(0, n), pref = rep(1, length(types)), directed = TRUE, attr = TRUE)
sample_cit_cit_types(n, edges = 1, types = rep(0, n), pref = matrix(1, nrow = length(types), ncol = length(types)), directed = TRUE, attr = TRUE)
Number of vertices.
Number of edges per step.
Number of aging bins.
sample_cit_typesor 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 (potentiall) cited
A new graph.