gnp_random_graph(n, p, seed=None, directed=False)
The $G_{n,p}$ model chooses each of the possible edges with probability $p$.
This algorithm runs in $O(n^2)$ time. For sparse graphs (that is, for small values of $p$), fast_gnp_random_graph
is a faster algorithm.
binomial_graph
and erdos_renyi_graph
are aliases for gnp_random_graph
.
>>> nx.binomial_graph is nx.gnp_random_graph True >>> nx.erdos_renyi_graph is nx.gnp_random_graph True
The number of nodes.
Probability for edge creation.
Indicator of random number generation state. See Randomness<randomness>
.
If True, this function returns a directed graph.
Returns a $G_{n,p}$ random graph, also known as an Erdős-Rényi graph or a binomial graph.
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.bipartite.generators.random_graph
networkx.generators.intersection.k_random_intersection_graph
networkx.generators.community.stochastic_block_model
networkx.generators.intersection.general_random_intersection_graph
networkx.generators.intersection.uniform_random_intersection_graph
networkx.generators.random_graphs.random_kernel_graph
networkx.generators.random_graphs.fast_gnp_random_graph
networkx.algorithms.approximation.clustering_coefficient.average_clustering
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