Random graph generation is an important tool for studying large complex networks. Despite abundance of random graph models, constructing models with application-driven constraints is poorly understood. In order to advance state-of-the-art in this area, we focus on random graphs without short...

In this work, we consider compound negative binomial and compound Poisson approximations to the generalized Poisson–binomial distribution. We derive some total variation upper bounds which improve on the existing results in terms of the order of approximation. An application is also discussed.