  The switching model is a Markov chain approach to sample graphs with fixed
degree sequence uniformly at random. The recently invented Curveball algorithm
for bipartite graphs applies several switches simultaneously (`trades'). Here,
we introduce Curveball algorithms for simple (un)directed graphs which use
single or simultaneous trades. We show experimentally that these algorithms
converge magnitudes faster than the corresponding switching models.
