Random k-regular network

Python

random_regular_graph[vert_type](size: int, degree: int, random_state) → undirected_network[vert_type]

C++

template<integer_network_vertex VertT, std::uniform_random_bit_generator Gen>
undirected_network<VertT> random_regular_graph(VertT size, VertT degree, Gen &generator)

Generates a random \(k\)-regular network, a random sample out of the space of all graphs where all nodes have exactly degree equal to degree.

>>> import reticula as ret
>>> gen = ret.mersenne_twister(42)
>>> ret.random_regular_graph[ret.int64](size=128, degree=4, random_state=gen)
<undirected_network[int64] with 128 verts and 256 edges>

The generation of a random \(k\)-regular graph becomes more and more difficult as the density increases. In C++, you can use the try_ variant of this function to limit runtime to a limited set of tries:

template<integer_network_vertex VertT, std::uniform_random_bit_generator Gen>
std::optional<undirected_network<VertT>> try_random_regular_graph(VertT size, VertT degree, Gen &generator, std::size_t max_tries)

If this function succeeds in max_tries tries, it will return the resulting network, otherwise it returns an instance of std::nullopt_t.