Generating random networks from a given distribution

Several variations are given for an algorithm that generates random networks approximately respecting the probabilities given by any likelihood function, such as from a p∗p∗ social network model. A novel use of the genetic algorithm is incorporated in these methods, which improves its applicability to the degenerate distributions that can arise with p∗p∗ models. Our approach includes a convenient way to find the high-probability items of an arbitrary network distribution function.