  We consider the following common network analysis problem: given a degree
sequence $\mathbf{d} = (d_1, \dots, d_n) \in \mathbb N^n$ return a uniform
sample from the ensemble of all simple graphs with matching degrees. In
practice, the problem is typically solved using Markov Chain Monte Carlo
approaches, such as Edge-Switching or Curveball, even if no practical useful
rigorous bounds are known on their mixing times. In contrast, Arman et al.
sketch Inc-Powerlaw, a novel and much more involved algorithm capable of
generating graphs for power-law bounded degree sequences with $\gamma
\gtrapprox 2.88$ in expected linear time.
  For the first time, we give a complete description of the algorithm and add
novel switchings. To the best of our knowledge, our open-source implementation
of Inc-Powerlaw is the first practical generator with rigorous uniformity
guarantees for the aforementioned degree sequences. In an empirical
investigation, we find that for small average-degrees Inc-Powerlaw is very
efficient and generates graphs with one million nodes in less than a second.
For larger average-degrees, parallelism can partially mitigate the increased
running-time.
