This set of graph games creates graphs directly through sampling of different attributes, topologies, etc. The nature of their algorithm is described in detail at the linked igraph documentation.

play_degree(out_degree, in_degree = NULL, method = "simple")

play_dotprod(position, directed = TRUE)

play_fitness(m, out_fit, in_fit = NULL, loops = FALSE,
multiple = FALSE)

play_fitness_power(n, m, out_exp, in_exp = -1, loops = FALSE,
multiple = FALSE, correct = TRUE)

play_erdos_renyi(n, p, m, directed = TRUE, loops = FALSE)

play_geometry(n, radius, torus = FALSE)

## Arguments

out_degree, in_degree The degrees of each node in the graph The algorithm to use for the generation. Either 'simple', 'vl', or 'simple.no.multiple' The latent position of each node by column. Should the resulting graph be directed The number of edges in the graph The fitness of each node Are loop edges allowed Are multiple edges allowed The number of nodes in the graph. Power law exponent of degree distribution Use finite size correction The probabilty of an edge occuring The radius within which vertices are connected Should the vertices be distributed on a torus instead of a plane

## Value

A tbl_graph object

## Functions

• play_degree: Create graphs based on the given node degrees. See igraph::sample_degseq()

• play_dotprod: Create graphs with link probability given by the dot product of the latent position of termintating nodes. See igraph::sample_dot_product()

• play_fitness: Create graphs where edge probabilities are proportional to terminal node fitness scores. See igraph::sample_fitness()

• play_fitness_power: Create graphs with an expected power-law degree distribution. See igraph::sample_fitness_pl()

• play_erdos_renyi: Create graphs with a fixed edge probability or count. See igraph::sample_gnp() and igraph::sample_gnm()

• play_geometry: Create graphs by positioning nodes on a plane or torus and connecting nearby ones. See igraph::sample_grg()

Other graph games: component_games, evolution_games, type_games
plot(play_erdos_renyi(20, 0.3)) 