Random Erdős–Rényi network¶

Undirected Erdős–Rényi network¶

Python

random_gnp_graph[vert_type](n: int, p: float, random_state) → undirected_network[vert_type]¶

C++

template<integer_network_vertex VertT, std::uniform_random_bit_generator Gen> undirected_network<VertT> random_gnp_graph(VertT n, double p, Gen &generator)¶

Generates a random \(G(n, p)\) graph of size n, 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++.

>>> import reticula as ret
>>> gen = ret.mersenne_twister(42)
>>> ret.random_gnp_graph[ret.int64](n=128, p=0.05, random_state=gen)
<undirected_network[int64] with 128 verts and 177 edges>

Directed Erdős–Rényi network¶

Python

random_directed_gnp_graph[vert_type](n: int, p: float, random_state) → directed_network[vert_type]¶

C++

template<integer_network_vertex VertT, std::uniform_random_bit_generator Gen> directed_network<VertT> random_directed_gnp_graph(VertT n, double p, Gen &generator)¶

Generates a random directed \(G(n, p)\) graph of size n, 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++.

>>> import reticula as ret
>>> gen = ret.mersenne_twister(42)
>>> ret.random_directed_gnp_graph[ret.int64](n=128, p=0.05, random_state=gen)
<directed_network[int64] with 128 verts and 345 edges>