This set of games are build around different types of nodes and simulating their interaction. The nature of their algorithm is described in detail at the linked igraph documentation.
n, n1, n2: The number of nodes in the graph. For bipartite graphs n1
and n2 specifies the number of nodes of each type.
n_types: The number of different node types in the graph
p_type: The probability that a node will be the given type. Either a vector or a matrix, depending on the game
p_pref: The probability that an edge will be made to a type. Either a vector or a matrix, depending on the game
fixed: Should n_types be understood as a fixed number of nodes for each type rather than as a probability
directed: Should the resulting graph be directed
loops: Are loop edges allowed
p: The probabilty of an edge occuring
m: The number of edges in the graph
mode: The flow direction of edges
growth: The number of edges added at each iteration
callaway: Use the callaway version of the trait based game
types: The type of each node in the graph, enumerated from 0
Returns
A tbl_graph object
Functions
play_preference(): Create graphs by linking nodes of different types based on a defined probability. See igraph::sample_pref()
play_preference_asym(): Create graphs by linking nodes of different types based on an asymmetric probability. See igraph::sample_asym_pref()
play_bipartite(): Create bipartite graphs of fixed size and edge count or probability. See igraph::sample_bipartite()
play_traits(): Create graphs by evolving a graph with type based edge probabilities. See igraph::sample_traits() and igraph::sample_traits_callaway()
play_citation_type(): Create citation graphs by evolving with type based linking probability. See igraph::sample_cit_types() and igraph::sample_cit_cit_types()
Examples
plot(play_bipartite(20,30,0.4))
See Also
Other graph games: component_games, evolution_games, sampling_games