  Generative network models play an important role in algorithm development,
scaling studies, network analysis, and realistic system benchmarks for graph
data sets. The commonly used graph-based benchmark model R-MAT has some
drawbacks concerning realism and the scaling behavior of network properties. A
complex network model gaining considerable popularity builds random hyperbolic
graphs, generated by distributing points within a disk in the hyperbolic plane
and then adding edges between points whose hyperbolic distance is below a
threshold.
  We present in this paper a fast generation algorithm for such graphs. Our
experiments show that our new generator achieves speedup factors of 3-60 over
the best previous implementation. One billion edges can now be generated in
under one minute on a shared-memory workstation. Furthermore, we present a
dynamic extension to model gradual network change, while preserving at each
step the point position probabilities.
