Random Erdős–Rényi network#
Undirected Erdős–Rényi network#
- random_gnp_graph[vert_type](size: int, p: float, random_state) -> undirected_network[vert_type]
-
template<integer_network_vertex VertT, std::uniform_random_bit_generator Gen>
undirected_network<VertT> random_gnp_graph(VertT size, double p, Gen &generator)#
Generates a random \(G(n = size, p)\) graph of size size, where every
edge exists independently with probability p
[2].
The expected number of edges is \(p \frac{n (n-1)}{2}\).
If the parameter p is not in the \([0, 1]\) range, the function fails
by raising a ValueError exception in Python or a
std::invalid_argument exception in C++.
Directed Erdős–Rényi network#
- random_directed_gnp_graph[vert_type](size: int, p: float, random_state) -> directed_network[vert_type]
-
template<integer_network_vertex VertT, std::uniform_random_bit_generator Gen>
directed_network<VertT> random_directed_gnp_graph(VertT size, double p, Gen &generator)#
Generates a random directed \(G(n = size, p)\) graph of size size,
where every directed edge exists independently with probability p
[2].
Note that unlike in an undirected network \((i, j)\) and \((j, i)\) are distinct edges in a directed network, so the expected number of edges is \(p n (n-1)\)
If the parameter p is not in the \([0, 1]\) range, the function fails
by raising a ValueError exception in Python or a
std::invalid_argument exception in C++.